Updated on 2023/01/05

写真a

 
MOROOKA Kenichi
 
Organization
Faculty of Natural Science and Technology Professor
Position
Professor
Contact information
メールアドレス
External link

Degree

  • 工学修士 ( 九州大学 工学部 情報工学科 )

  • 博士(工学) ( 九州大学大学院 システム情報科学研究科 知能システム学専攻 )

Research Interests

  • Medical Image Processing

  • Computational anatomy

  • Computer aided system for therapy and surgery

Research Areas

  • Informatics / Perceptual information processing  / Image Information Processing

  • Informatics / Life, health and medical informatics  / Medical Image

  • Life Science / Medical systems  / Computer Aided Surgery

Education

  • Kyushu University   システム情報科学研究院   知能システム学専攻 博士後期課程

    1997.4 - 2000.3

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  • Kyushu University   システム情報科学研究科   知能システム学専攻 修士課程

    1995.4 - 1997.3

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    Country: Japan

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  • Kyushu University   工学部   情報工学科

    1991.4 - 1995.3

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    Country: Japan

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Research History

  • Okayama University   Graduate School of Natural Science and Technology   Professor

    2020.4

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  • Kyushu University   Graduate School of Information Science and Electrical Engineering   Associate Professor

    2010.10 - 2020.3

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  • Kyushu University   Graduate School of Medical Sciences   Associate Professor

    2010.7 - 2010.9

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  • Kyushu University   Digital Medicine Initiative   Associate Professor

    2006.2 - 2010.6

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  • Tokyo Institute of Technology   像情報工学研究施設   Research Assistant

    2000.5 - 2006.1

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  • (財)九州システム技術研究所   特別研究員

    2000.4

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Professional Memberships

Committee Memberships

  • IEEE EMBS West Japan Chapter   Chair  

    2021.1   

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    Committee type:Other

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  • 電子情報通信学会 画像工学研究会   専門委員  

    2017.6   

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    Committee type:Academic society

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  • IEEE/RSJ International Conference on Intelligent Robots and Systems   Associate Editor  

    2016.9 - 2017.9   

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    Committee type:Academic society

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  • International Forum on Medical Imaging in Asia 2017   Program committee  

    2016.1 - 2017.1   

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    Committee type:Academic society

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  • 生体医工シンポジウム2015   プログラム委員  

    2014.9 - 2015.9   

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    Committee type:Academic society

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  • 日本コンピュータ外科学会   評議員  

    2013.11   

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    Committee type:Academic society

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  • 7th Biomedical Engineering International Conference   Local Organizing Committee  

    2013.11 - 2014.11   

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    Committee type:Academic society

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  • 電子情報通信学会 医用画像工学研究会   専門委員  

    2013.6   

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    Committee type:Academic society

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  • 生体医工シンポジウム2013   組織&プログラム委員  

    2012.9 - 2013.9   

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    Committee type:Academic society

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  • 第52回日本生体医工学会大会   組織委員  

    2012.7 - 2013.7   

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    Committee type:Academic society

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  • 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society   Local Program Commitee  

    2012.7 - 2013.7   

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    Committee type:Academic society

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  • 2012 IEEE/SICE International Symposium on System Integration   Local Arrangement Chair  

    2011.12 - 2012.12   

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    Committee type:Academic society

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  • 第13回 計測自動制御学会 システムインテグレーション部門講演会   プログラム副委員長  

    2011.12 - 2012.12   

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  • International Forum on Medical Imaging in Asia 2012   Program committee  

    2011.11 - 2012.11   

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  • The 8th International Forum on Multimedia and Image Processing   Program committee  

    2011.6 - 2012.6   

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    Committee type:Academic society

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  • 第51回日本生体医工学会大会   実行委員  

    2011.5 - 2012.5   

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    Committee type:Academic society

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  • 第17回 ロボティクスシンポジア   プログラム委員  

    2011.3 - 2012.3   

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  • IEEE West Japan Chapter of Engineering in Medicine and Biology   Vice Chair  

    2011.1 - 2014.12   

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    Committee type:Academic society

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  • 電子情報通信学会   常任査読委員  

    2010.8   

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    Committee type:Academic society

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  • 第19回日本コンピュータ外科学会   大会実行委員  

    2009.11 - 2010.11   

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  • 第25回生体生理工学シンポジウム   セッションオーガナイザー  

    2009.9 - 2010.9   

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  • 日本生体医工学会   評議員  

    2009.4   

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  • IEEE West Japan Chapter of Engineering in Medicine and Biology   Secretary/Treasurer  

    2007.5 - 2010.12   

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  • ACCV'07 Workshop on Multi-dimensional and Multi-view Image Processing   Program committee  

    2006.11 - 2007.11   

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  • 電子情報通信学会 画像工学研究会   専門委員  

    2005.6 - 2010.5   

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  • 視聴覚情報研究会   幹事  

    2004.5 - 2006.1   

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Papers

  • Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images

    Ryota Nakanishi, Ken’ichi Morooka, Kazuki Omori, Satoshi Toyota, Yasushi Tanaka, Hirofumi Hasuda, Naomichi Koga, Kentaro Nonaka, Qingjiang Hu, Yu Nakaji, Tomonori Nakanoko, Koji Ando, Mitsuhiko Ota, Yasue Kimura, Eiji Oki, Yoshinao Oda, Tomoharu Yoshizumi

    Annals of Surgical Oncology   2022.12

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1245/s10434-022-12926-x

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    Other Link: https://link.springer.com/article/10.1245/s10434-022-12926-x/fulltext.html

  • Automatic electron hologram acquisition of catalyst nanoparticles using particle detection with image processing and machine learning

    Fumiaki Ichihashi, Akira Koyama, Tetsuya Akashi, Shoko Miyauchi, Ken'ichi Morooka, Hajime Hojo, Hisahiro Einaga, Yoshio Takahashi, Toshiaki Tanigaki, Hiroyuki Shinada, Yasukazu Murakami

    Applied Physics Letters   120 ( 6 )   064103 - 064103   2022.2

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    Publishing type:Research paper (scientific journal)   Publisher:AIP Publishing  

    DOI: 10.1063/5.0074231

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  • Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution

    Jun Mutaguchi, Ken`ichi Morooka, Satoshi Kobayashi, Aiko Umehara, Shoko Miyauchi, Fumio Kinoshita, Junichi Inokuchi, Yoshinao Oda, Ryo Kurazume, Masatoshi Eto

    Journal of Endourology   2022.1

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    Authorship:Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:Mary Ann Liebert Inc  

    DOI: 10.1089/end.2021.0483

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  • Analysis of TEM images of metallic nanoparticles using convolutional neural networks and transfer learning Reviewed

    Akira Koyama, Shoko Miyauchi, Ken'ichi Morooka, Hajime Hojo, Hisahiro Einaga, Yasukazu Murakami

    Journal of Magnetism and Magnetic Materials   538   168225 - 168225   2021.11

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    Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.jmmm.2021.168225

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  • Automatic Hologram Acquisition of Pt Catalyst Nanoparticles on TiO2 Using Particle Detection with Image Processing and AI Classification Reviewed

    Fumiaki Ichihashi, Akira Koyama, Tetsuya Akashi, Shoko Miyauchi, Ken'ichi Morooka, Hajime Hojo, Hisahiro Einaga, Toshiaki Tanigaki, Hiroyuki Shinada, Yasukazu Murakami

    Microscopy and Microanalysis   27 ( S1 )   252 - 253   2021.8

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  • Motion Generation by Learning Relationship between Object Shapes and Human Motions

    Tokuo Tsuji, Sho Tajima, Yosuke Suzuki, Tetsuyou Watanabe, Shoko Miyauchi, Ken'ichi Morooka, Kensuke Harada, Hiroaki Seki

    Proceedings of International Conference on Artificial Life and Robotics   26   332 - 335   2021.1

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    Publishing type:Research paper (scientific journal)   Publisher:ALife Robotics Corporation Ltd.  

    DOI: 10.5954/icarob.2021.os11-3

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  • A deep learning-based method for predicting volumes of nasopharyngeal carcinoma for adaptive radiation therapy treatment Reviewed

    Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    25th International Conference on Pattern Recognition(ICPR)   3256 - 3263   2021.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/ICPR48806.2021.9412924

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    Other Link: https://dblp.uni-trier.de/db/conf/icpr/icpr2020.html#DaoudMMKMFD20

  • PO-1668: Can we use cascade deep learning for GTV delineation in adaptive radiotherapy for NPC?

    B. Daoud, K. Morooka, R. Kurazume, N. Fourati, W. Mnejja, L. Farhat, J. Daoud

    Radiotherapy and Oncology   152   S916 - S916   2020.11

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    DOI: 10.1016/s0167-8140(21)01686-8

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  • A Method for Predicting Dose Distribution of Nasopharyngeal Carcinoma Cases by Multiple Deep Neural Networks Reviewed

    Bilel Daoud, Ken'Ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020   2020.8

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    In this paper, we propose a method for predicting dose distribution images of patients with Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed system is based on our previous method [1]. The first phase is to obtain the feature maps of 2D dose images of each beam from contoured CT images of a patient by convolutional deep neural network model. In the second phase, dose distribution images are predicted from the obtained feature maps by the integration network. Our modified system predicted dose distribution images accurately. From the experimental results using 80 NPC patients' images, the average number of pixels that satisfy the dose constraints of tumors and OARs regions is 81.9 % and 86.1 %, respectively. The proposed system had a global 3D gamma passing rates varying from 82.1 % to 97.2 % for all regions and an overall mean absolute errors (MAEs) was 1.0 ±1.2. From the obtained results, our modified system is superior to the results obtained in our previous system results and conventional methods. Contribution-The use of the predicted 7-beam weights, as input, into our CNN network leads to improve the predicted dose distribution. Contribution-The use of the predicted 7-beam weights, as input, into our CNN network leads to improve the predicted dose distribution.

    DOI: 10.1109/ICIEVicIVPR48672.2020.9306610

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  • Automatic segmentation of nasopharyngeal carcinoma from CT images Reviewed

    Bilel Daoud, Ali Khalfallah, Leila Farhat, Wafa Mnejja, Ken'ichi Morooka, Med Salim Bouhlel, Jamel Daoud

    International Journal of Biomedical Engineering and Technology   33 ( 3 )   240 - 257   2020.3

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:INDERSCIENCE ENTERPRISES LTD  

    This paper presents an automatic segmentation technique for identifying nasopharyngeal carcinoma regions in CT images. The proposed technique is based on the region growing method by which an initial seed is automatically generated. A probabilistic map representing a chance of being the tumor pixel in each CT image will be created and used for initial seed determination. This map is generated from three probabilistic functions established upon location of the tumor considered, intensities of the tumor pixels, and asymmetry of organs respectively. A representative of potential tumor pixels will be selected as an initial seed. The experimental results showed that seeds were correctly determined with the percent accuracy of 84.32%. These seeds were grown in preprocessed CT images for identifying the nasopharyngeal carcinoma regions subsequently. The results showed that, for no metastasis cases, perfect match and corresponding ratio were 85.03% and 52.44% respectively and 29.26% and 28.03% correspondingly for metastasis cases. This resulted from a single seed generated in each CT image. It was unable to identify more than one tumor region.

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  • Brain volume mapping for constructing volumetric statistical shape model Reviewed

    Shoko Miyauchi, Ken'ichi Morooka, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    International Forum on Medical Imaging in Asia 2019   2019.3

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    Authorship:Corresponding author   Publishing type:Research paper (international conference proceedings)   Publisher:SPIE  

    DOI: 10.1117/12.2519819

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  • Ancient Pelvis Reconstruction From Collapsed Component Bones Using Statistical Shape Models Reviewed

    Ken’ichi Morooka, Ryota Matsubara, Shoko Miyauchi, Takaichi Fukuda, Takeshi Sugii, Ryo Kurazume

    Machine Vision and Applications   30 ( 1 )   59 - 69   2019.2

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:SPRINGER  

    We propose a new method for recovering the pelvis of an ancient skeleton from its three component bones with collapsed surfaces. The proposed method uses four types of statistical shape models (SSMs) for the bones. The SSM for each bone describes the mean shape and shape variations of a class of bones. The SSMs for the three component bones are employed to restore the shapes of the component bones. The SSM for the whole pelvis provides the natural anatomical shape of the pelvis and the spatial relationship among the sacrum and the hip bones. Therefore, the three component bones are aligned by using the SSM for the pelvis. The experimental results show that our method achieves reliable reconstruction of the ancient pelvis shape despite having collapsed surfaces in its component bones.

    DOI: 10.1007/s00138-018-0972-5

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  • 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning. Reviewed

    Bilel Daoud, Ken'ichi Morooka, Ryo Kurazume, Leila Farhat, Wafa Mnejja, Jamel Daoud

    Comput. Medical Imaging Graph.   77   2019

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    DOI: 10.1016/j.compmedimag.2019.101644

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  • GAN-Based Method for Synthesizing Multi-focus Cell Images. Reviewed

    Ken'ichi Morooka, Xueru Zhang, Shoko Miyauchi, Ryo Kurazume, Eiji Ohno

    Image and Video Technology - PSIVT 2019 International Workshops   100 - 107   2019

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    DOI: 10.1007/978-3-030-39770-8_8

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  • Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma. Reviewed

    Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    Artificial Intelligence in Radiation Therapy - First International Workshop(AIRT@MICCAI)   128 - 136   2019

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-030-32486-5_16

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  • 3D Image Reconstruction from Multi-focus Microscopic Images. Reviewed

    Takahiro Yamaguchi, Hajime Nagahara, Ken'ichi Morooka, Yuta Nakashima, Yuki Uranishi, Shoko Miyauchi, Ryo Kurazume

    Image and Video Technology - PSIVT 2019 International Workshops   73 - 85   2019

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-030-39770-8_6

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  • Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain Reviewed

    Aoi Shinjo, Atsushi Saito, Tetsuya Takakuwa, Shigehito Yamada, Hidekata Hontani, Hiroshi Matsuzoe, Shoko Miyauchi, Kenichi Morooka, Akinobu Shimizu

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   11840 LNCS   94 - 103   2019

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    We propose a new method for constructing a spatiotemporal statistical model of the distribution of anatomical landmarks (LMs) of a human embryo. This method exhibits potential for the quantitative assessment of the extent of anomalies and is important in the research of congenital malformations. However, a few of the LMs might not be observed at a specific developmental stage because large morphological deformations exist during the early stages of development. It is difficult for conventional statistical shape analysis methods to handle missing LMs in the training dataset. The basic concept of the proposed method is to conduct statistical analyses by predicting and completing the coordinates of the missing LMs. We demonstrated the proposed method in the context of spatiotemporal statistical modeling of 10 LMs on the brain surface using 37 embryonic subjects with Carnegie stages of 19–22. We conducted a comparative study of the spatiotemporal statistical models between four different prediction methods, and we found that deformable surface mapping was the best prediction method in terms of model generalization and specificity.

    DOI: 10.1007/978-3-030-32689-0_10

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    Other Link: https://dblp.uni-trier.de/db/conf/miccai/clip2019.html#ShinjoSTYHMMMS19

  • Hexahedron Model Generation of Human Organ by Self-Organizing Deformable Model Reviewed

    Ken'ichi Morooka, Shoko Miyauchi, Xian Chen, Ryo Kurazume

    World Automation Congress Proceedings   2018-June   188 - 192   2018.8

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    © 2018 TSI Press. This paper presents a new method for generating object mesh models composed of hexahedra. The proposed method is based on a self-organizing deformable model (SDM) which is a deformable surface model guided by competitive learning and an energy minimization approach. Extending the SDM, the proposed method generates a hexahedral mesh model of a target object by fitting a cuboid composed of rectangular voxels to the object. Moreover, the shape of each hexahedron in the model is corrected by dividing the hexahedron into sub-hexahedra and moving the nodes of the hexahedron. From our experimental results, the proposed method obtains the hexahedral mesh model which consists of many regular hexahedra while recovering the shape of the object.

    DOI: 10.23919/WAC.2018.8430386

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  • Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies Reviewed International journal

    Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    Computer Methods and Programs in Biomedicine   157   237 - 250   2018.4

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    © 2018 Elsevier B.V. Background and Objective: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs. Methods: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM. Results: From the mapping results of various organ models with different number of holes, it is observed that Fast mSDM can map the organ models onto their target surfaces efficiently and stably without foldovers while preserving geometrical features. Conclusions: Fast mSDM can map the organ model onto the target surface efficiently and stably, and is applicable to medical applications including Statistical Shape Model.

    DOI: 10.1016/j.cmpb.2018.01.028

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  • Grasp Synergy Analysis Based on Contact Area of Fingers using Thermal Signatures Reviewed

    Tokuo Tsuji, Hidetoshi Seki, Daisuke Inada, Ken-ichi Morooka, Kensuke Harada, Kenji Tahara, Masatoshi Hikizu, Hiroaki Seki

    2018 57TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE)   1386 - 1392   2018

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    We propose a new method for analyzing human grasping motion using an infrared camera. This technique simplifies the teaching of motion to robots based on the observation of human motion. In this method, the contact area on the object is extracted by observing the thermal signature captured by an infrared camera. To understand the intention of human behavior, we propose a grasping identification method using 3D thermal signatures. In addition, we perform a principal component analysis on the contact area and the center of gravity for the contact area of each finger. This method expresses the grasp motion space with a small number of parameters and can be used to enable easy correspondence between human and robot hands. We confirm experimentally that the proposed method is effective for teaching motion to robot.

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  • Medical Support System by Estimating Organ Deformation Using Deep Neural Network Based on Finite Element Method

    MOROOKA Ken’ichi, KOBAYASHI Kaoru

    Medical Imaging Technology   35 ( 4 )   206 - 211   2017

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    Language:Japanese   Publisher:The Japanese Society of Medical Imaging Technology  

    There are support systems for surgery using 3D object models of human organs such as surgical simulation and preoperative surgical planning. One of fundamental techniques in the support systems is to estimate the organ deformation in real-time. Finite element method (FEM) is one of well-known techniques for accurately simulating the physical behaviors of objects. However, FE analysis requires substantial computational expenses to obtain more realism simulation. To solve the problem, we have been constructing neural networks to estimate the nonlinear organ deformation. By using the training data generated by nonlinear FEM, the network learns the organ deformation when an external force acts on the organ surface. The computations in the network is the weighted sum of simple nonlinear functions. Therefore, our method achieves the real-time FE analysis while keeping the analysis accuracy. In this paper, we show the overview of our method and the experimental results obtained by our method.

    DOI: 10.11409/mit.35.206

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  • Angle- and Volume-Preserving Mapping of Organ Volume Model Based on modified Self-organizing Deformable Model Reviewed

    Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)   2204 - 2209   2016.12

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE COMPUTER SOC  

    This paper proposes a new method for mapping volume models of human organs onto a target volume with simple shapes. The proposed method is based on our modified Self-organizing Deformable Model (mSDM) which finds the one-to-one mapping with no foldovers between an arbitrary object surface model and a target surface. By extending mSDM to apply to organ volume models, the proposed method, called volumetric SDM (vSDM), establishes the one-to-one correspondence between the volume model and its target volume. At the same time, vSDM preserves geometrical properties of the original model before and after the mapping. In addition, vSDM allows to control the mapping of interior structures of the organ model onto specific regions inside the target volume. These characteristics of vSDM enables to easily find a reliable correspondence between different volume models via a common target volume.

    DOI: 10.1109/ICPR.2016.7899963

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  • Volume Representation of Parenchymatous Organs by Volumetric Self-organizing Deformable Model Reviewed

    Shoko Miyauchi, Ken’ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    MICCAI 2016 Workshop on Spectral and Shape Analysis in Medical Imaging   10126 LNCS   39 - 50   2016.10

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

    This paper proposes a new method for describing parenchymatous organs by the set of volumetric primitives with simple shapes. The proposed method is based on our modified Self-organizing Deformable Model (mSDM) which maps an object surface model onto a target surface with no foldovers. By extending mSDM to apply to organ volume models, the proposed method, volumetric SDM (vSDM), finds the one-to-one correspondence between the volume model and its target volume. During the mapping, vSDM preserves geometrical properties of the original model while mapping internal structures of the model onto their corresponding primitives inside of the target volume. Owing to these characteristics, vSDM enables to obtain a new volume representation of organ volume models which simultaneously (1) represents by simple primitives the shapes of the whole organ and its internal structures and (2) describes the relationship among the external surface and internal structures of the organ.

    DOI: 10.1007/978-3-319-51237-2_4

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  • A method for mapping tissue volume model onto target volume using volumetric self-organizing deformable model. Reviewed

    Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    Medical Imaging 2016: Image Processing, San Diego, California, USA, February 27, 2016   9784   97842Z   2016

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:SPIE  

    DOI: 10.1117/12.2217367

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  • Service robot system with an informationally structured environment Reviewed

    Yoonseok Pyo, Kouhei Nakashima, Shunya Kuwahata, Ryo Kurazume, Tokuo Tsuji, Ken'ichi Morooka, Tsutomu Hasegawa

    ROBOTICS AND AUTONOMOUS SYSTEMS   74   148 - 165   2015.12

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:ELSEVIER  

    Daily life assistance is one of the most important applications for service robots. For comfortable assistance, service robots must recognize the surrounding conditions correctly, including human motion, the position of objects, and obstacles. However, since the everyday environment is complex and unpredictable, it is almost impossible to sense all of the necessary information using only a robot and sensors attached to it. In order to realize a service robot for daily life assistance, we have been developing an informationally structured environment using distributed sensors embedded in the environment. The present paper introduces a service robot system with an informationally structured environment referred to the ROS-TMS. This system enables the integration of various data from distributed sensors, as well as storage of these data in an on-line database and the planning of the service motion of a robot using real-time information about the surroundings. In addition, we discuss experiments such as detection and fetch-and-give tasks using the developed real environment and robot. (C) 2015 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.robot.2015.07.010

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  • An Informationally Structured Room for Robotic Assistance (dagger) Reviewed

    Tsuji, Tokuo, Mozos, Oscar Martinez, Chae, Hyunuk, Pyo, Yoonseok, Kusaka, Kazuya, Hasegawa, Tsutomu, Morooka, Ken'ichi, Kurazume, Ryo

    SENSORS   15 ( 4 )   9438 - 9465   2015.4

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    The application of assistive technologies for elderly people is one of the most promising and interesting scenarios for intelligent technologies in the present and near future. Moreover, the improvement of the quality of life for the elderly is one of the first priorities in modern countries and societies. In this work, we present an informationally structured room that is aimed at supporting the daily life activities of elderly people. This room integrates different sensor modalities in a natural and non-invasive way inside the environment. The information gathered by the sensors is processed and sent to a centralized management system, which makes it available to a service robot assisting the people. One important restriction of our intelligent room is reducing as much as possible any interference with daily activities. Finally, this paper presents several experiments and situations using our intelligent environment in cooperation with our service robot.

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  • Motion Planning for Fetch-and-Give Task using Wagon and Service Robot Reviewed

    Yoonseok Pyo, Kouhei Nakashima, Tokuo Tsuji, Ryo Kurazume, Ken'ichi Morooka

    2015 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)   925 - 932   2015

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    Daily life assistance for elderly individuals in hospitals and care facilities is one of the most urgent and promising applications for service robots. Especially, a fetchand- give task is a frequent and fundamental task for service robots to assist elderlys daily life. In hospitals and care facilities, this task is often performed with a movable platform such as a wagon or a cart to carry and deliver a large amount of objects at once. Thus the navigation and control of not only a service robot but also a movable platform must be planned safely. In addition, a robot motion planning to hand over an object to a person safely and comfortably according to his/her posture is also an important problem in this task, however this has not been discussed so much. In this work, we present a coordinate motion planning technique for a fetch-and-give task using a wagon and a service robot. Handover motion is also planned by considering the manipulability of both a robot and a person. Experiments of a fetch-and-give task using a service robot are successfully carried out.

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  • Grasp Stability Evaluation based on Energy Tolerance in Potential Field Reviewed

    Tsuji, Tokuo, Baba, Kosei, Tahara, Kenji, Harada, Kensuke, Morooka, Ken'ichi, Kurazume, Ryo

    2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)   2015-December   2311 - 2316   2015

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    We propose an evaluation method of grasp stability which takes into account the elastic deformation of fingertips from the viewpoint of energy. An evaluation value of grasp stability is derived as the minimum energy which causes slippage of a fingertip on its contact surface. To formulate the evaluation value, the elastic potential energy of fingertips and the gravitational potential energy of a grasped object are considered. It is ensured that fingertips do not slip on grasped object surfaces if the external energy applied to the object is less than the evaluation value. Since our evaluation value explicitly considers the deformation values of fingertips, grasp stability is evaluated by taking into consideration the contact forces generated by the deformation. The effectiveness of our method is verified through numerical examples.

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  • Area- and angle-preserving parameterization for vertebra surface mesh

    Shoko Miyauchi, Ken’Ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    Lecture Notes in Computational Vision and Biomechanics   20   187 - 198   2015

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    This paper proposes a parameterization method of vertebra models by mapping them onto the parameterized surface of a torus. Our method is based on a modified Self-organizing Deformable Model (mSDM) [1], which is a deformable model guided by competitive learning and an energy minimization approach. Unlike conventional mapping methods, the mSDM finds the one-to-one mapping between arbitrary surface model and the target surface with the same genus as the model. At the same time, the mSDM can preserve geometrical properties of the original model before and after mapping. Moreover, users are able to control mapping positions of the feature vertices in the model. Using the mSDM, the proposed method maps the vertebra model onto a torus surface through an intermediate surface with the approximated shape of the vertebra. The use of the intermediate surface results in the stable mapping of the vertebra to a torus compared with the direct mapping from the model to the torus.

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  • Area- and angle-preserving parameterization for vertebra surface mesh

    Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

    Lecture Notes in Engineering and Computer Science   20   187 - 198   2015

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    This paper proposes a parameterization method of vertebra models by mapping them onto the parameterized surface of a torus. Our method is based on a modified Self-organizing Deformable Model (mSDM) [1], which is a deformable model guided by competitive learning and an energy minimization approach. Unlike conventional mapping methods, the mSDM finds the one-to-one mapping between arbitrary surface model and the target surface with the same genus as the model. At the same time, the mSDM can preserve geometrical properties of the original model before and after mapping. Moreover, users are able to control mapping positions of the feature vertices in the model. Using the mSDM, the proposed method maps the vertebra model onto a torus surface through an intermediate surface with the approximated shape of the vertebra. The use of the intermediate surface results in the stable mapping of the vertebra to a torus compared with the direct mapping from the model to the torus.

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  • A method for identifying distribution pattern of cone cells in retina image

    Ken'ichi Morooka, Yuanting Ji, Oscar Martinez Mozos, Tokuo Tsuji, Ryo Kurazume, Peter K. Ahnelt

    World Automation Congress Proceedings   774 - 778   2014.10

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    This paper proposes a method to identify the spatial distribution patterns of cone cells related with blood vessel in a given retina image. We define three types of the distribution patterns between cones and vessels. Positive correlation distribution (PCD) and negative correlation distribution (NCD) indicate that the cones tend to be close to or far from the vessels. While the cone cells do not have significant correlation with vessels, the cone distribution is regarded as the random distribution (RD). In our method, RD is modeled by many virtual retina images, each of which is generated by the vessels extracted from the original retina image and the virtual cells are selected randomly from the image. Using the virtual images, we estimate the distribution range of RD. When the distribution of the original cells is above the upper limit or below the lower limit of the RD distribution, the cell distribution is NCD or PCD. Otherwise, the cell distribution is regarded as RD.

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  • 3D Model Generation of Brain Block Using Internal Structure Contours for 3D Japanese Brain Atlas Construction (Medical Imaging) Reviewed

    SASAKI Shou, MOROOKA Ken'ichi, KOBAYASHI Kaoru, TSUJI Tokuo, MIYAGI Yasushi, FUKUDA Takaichi, SAMURA Kazuhiro, KURAZUME Ryo

    IEICE technical report.   114 ( 103 )   1 - 6   2014.6

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    The digital human modeling is one of the challenging topics in the last few decades. We have been studying on the digital brain atlas of Japanese. Our system for generating the brain atlas uses the latest techniques in both human anatomy and computer science. This paper reports the detail of generating the brain model by the 3D contour models of the brain surface and internal neural structures. Especially, we propose a new method for registering two sub-block models of the brain by combining a nonrigid registration and dynamic programming.

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  • A Method for Reconstructing 3D Tissue Shapes from Stereo Endoscopic Images Using Wide-Range Edge Reviewed

    Computer Assisted Radiology and Surgery   2014.6

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  • Floor Sensing System Using Laser Reflectivity for Localizing Everyday Objects and Robot Reviewed

    Yoonseok Pyo, Tsutomu Hasegawa, Tokuo Tsuji, Ryo Kurazume, Ken'ichi Morooka

    SENSORS   14 ( 4 )   7524 - 7540   2014.4

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    This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall. Moreover, since the area of sensing is limited to a LRF scanning plane parallel to the floor and just a few centimeters above the floor, the scanning covers the whole room with minimal invasion of privacy of a resident, and occlusion problem is mitigated by using mirror. We use the reflection intensity and position information obtained from the target surface. Although it is not possible to identify all objects by additionally using reflection values, it would be easier to identify unknown objects if we can eliminate easily identifiable objects by reflectance. In addition, we propose a method for measuring the robot's pose using the tag which has the encoded reflection pattern optically identified by the LRF. Our experimental results validate the effectiveness of the proposed method.

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  • Grasp Stability Analysis for Elastic Fingertips by using Potential Energy Reviewed

    Tokuo Tsuji, Kosei Baba, Kenji Tahara, Kensuke Harada, Ken'ichi Morooka, Ryo Kurazume

    2014 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII)   453 - 458   2014

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    We propose a grasp stability index which takes into account the elastic deformation of fingertips. In this index, an evaluation value of grasp stability is derived as the minimum energy which causes slippage of a fingertip on a grasped object surface. Elastic potential energy of the fingertips and gravitational potential energy of the object are considered in this index. It is ensured that any fingertip does not slip on a grasped object surface if the external energy added to the object is less than the evaluation value. This index has good features as follows. (i) Contact forces corresponding to the grasping state are taken into account. (ii) It is possible to derive a condition of stable grasp including the kinetic energy of a grasped object. The effectiveness of this index is verified through several numerical examples.

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  • Grasp Planning for Constricted Parts of Objects Approximated with Quadric Surfaces Reviewed

    Tokuo Tsuji, Soichiro Uto, Kensuke Harada, Ryo Kurazume, Tsutomu Hasegawa, Ken'ichi Morooka

    2014 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2014)   2447 - 2453   2014

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    This paper presents a grasp planner which allows a robot to grasp the constricted parts of objects in our daily life. Even though constricted parts can be grasped more firmly than convex parts, previous planners have not sufficiently focused on grasping this part. We develop techniques for quadric surface approximation, grasp posture generation, and stability evaluation for grasping constricted parts. By modeling an object into multiple quadric surfaces, the planner generates a grasping posture by selecting one-sheet hyperbolic surfaces or two adjacent ellipsoids as constricted parts. When a grasping posture being generated, the grasp stability is evaluated based on the distribution of the stress applied to an object by the fingers. We perform several simulations and experiments to verify the effectiveness of our proposed method.

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  • Navigation system with real-time finite element analysis for minimally invasive surgery. Reviewed

    Ken'ichi Morooka, Yousuke Nakasuka, Ryo Kurazume, Xian Chen, Tsutomu Hasegawa, Makoto Hashizume

    35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, July 3-7, 2013   2013   2996 - 2999   2013

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  • A method for estimating patient specific parameters for simulation of tissue deformation by finite element analysis Reviewed

    Ken'ichi Morooka, Shuji Sonoki, Ryo Kurazume, Tsutomu Hasegawa

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   7930 ( 1 )   113 - 120   2013

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    This paper proposes a method for estimating patient-specific material parameters used in the finite element analysis which simulates soft tissue deformation. The estimation of suitable material parameters for a patient is important for a navigation system for endoscopic surgery. At first, many data of soft tissue deformation are generated by changing the material parameters. Next, using Principle Component Analysis, each data with high dimensional is converted into the lower vector. The relationship between the material parameter and the deformation is found in the lower potential space. © 2013 Springer-Verlag.

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  • The Intelligent Room for Elderly Care. Reviewed

    Óscar Martínez Mozos, Tokuo Tsuji, Hyunuk Chae, Shunya Kuwahata, YoonSeok Pyo, Tsutomu Hasegawa, Ken'ichi Morooka, Ryo Kurazume

    Natural and Artificial Models in Computation and Biology - 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, Spain, June 10-14, 2013. Proceedings, Part I   7930 LNCS ( PART 1 )   103 - 112   2013

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    Daily life assistance for elderly is one of the most promising and interesting scenarios for advanced technologies in the near future. Improving the quality of life of elderly is also some of the first priorities in modern countries and societies where the percentage of elder people is rapidly increasing due mainly to great improvements in medicine during the last decades. In this paper, we present an overview of our informationally structured room that supports daily life activities of elderly with the aim of improving their quality of life. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is sent to a centralized management system which processes the data and makes it available to a service robot which assists the people in the room. One important restriction in our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activities of people and to reduce as much as possible the invasion of their privacy. In addition we discuss some experiments using our real environment and robot. © 2013 Springer-Verlag.

    DOI: 10.1007/978-3-642-38637-4_11

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  • Measurement and Estimation of Indoor Human Behavior of Everyday Life Based on Floor Sensing with Minimal Invasion of Privacy Reviewed

    Yoonseok Pyo, Tsutomu Hasegawa, Masahide Tanaka, Tokuo Tsuji, Ken'ichi Morooka, Ryo Kurazume

    2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO)   2170 - 2176   2013

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    This paper describes a method of measurement and estimation of human behaviors in a room together with the layout of objects on the floor. The information obtained by the method is essential for a service robot working in a human daily life environment. The method uses only one laser range finder (LRF) installed in the room and a strip of mirror attached to a side wall close to a floor. The area of sensing is limited to a plane parallel to and just a few centimeters above the floor, thus covering the whole room with minimal invasion of privacy of a resident while reducing occlusion. Processing both distance and reflectance acquired by the LRF from the surface of the existing objects allows us to exclude immediately distinguishable clusters and to focus on the analysis of remaining clusters. The human behavior models that we propose are effectively used to estimate human behavior based on the limited LRF data. Our experimental results validate the effectiveness of the proposed method.

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  • Tissue Surface Model Mapping onto Arbitrary Target Surface Based on Self-organizing Deformable Model Reviewed

    Shoko Miyauchi, Ken'ichi Morooka, Yasushi Miyagi, Takaichi Fukuda, Tokuo Tsuji, Ryo Kurazume

    2013 FOURTH INTERNATIONAL CONFERENCE ON EMERGING SECURITY TECHNOLOGIES (EST)   79 - 82   2013

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    This paper proposes a new method for mapping a tissue surface model onto an arbitrary target surface while preserving the geometrical features of the tissue surface. In our method, firstly, the tissue model is roughly deformed by using Self-organizing Deformable Model. Since the deformed model may contain folded patches, the folded patches are removed. Moreover, by Free-Form Deformation (FTD), and the area- and angle-preserving mapping, the model is mapped onto the target surface while preserving geometrical properties of the original model. From several experimental results, we can conclude that the proposed method can map tissue models onto arbitrary target surface without foldovers.

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  • A method for selecting landmark to generate patient-specific brain atlas by deforming standard brain atlas

    Kaoru Kobayashi, Ken'ichi Morooka, Yasushi Miyagi, Takaichi Fukuda, Tokuo Tsuji, Ryo Kurazume

    Transactions of Japanese Society for Medical and Biological Engineering   51 ( 6 )   390 - 396   2013

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    To estimate the internal structures of the patient brain, several methods map a brain atlas to a patient brain shape using some landmarks, which are selected manually from the brain shape. The determination of the correspondence between small sulci of arbitrary two brains is complex. Moreover, the relationship between the surface shape and internal structure of the brain is unclear. Therefore, even if the surface the deformed atlas is fitted to the patient brain shape, the accurate internal structures of the brain can not always be obtained. To solve these problems, this paper proposes a method of selecting landmarks to estimate the internal structures of the patient brain by deforming brain atlas. Firstly, the brain shapes are represented by a simple shape. Secondly, a small number of initial landmarks are selected manually from the contours of the approximated brain shape and the internal structures which can be identified from MR images. Thirdly, some new landmarks are generated automatically on the contours based on the initial landmarks. Finally, the brain atlas is deformed non-rigidly using these landmarks. To evaluate our method, we estimate the patient brain structure using 4 methods. From the experimental results using 10 brain images, our method can estimate the patient brain structure reliably and stably using a small number of landmarks compared with the method using the landmarks on the original brain shape.

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  • A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks. Reviewed

    Ken'ichi Morooka, Tomoyuki Taguchi, Xian Chen, Ryo Kurazume, Makoto Hashizume, Tsutomu Hasegawa

    Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, 4-9 February 2012   8316   83160J   2012

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    This paper presents a method for simulating the behavior of stomach with large-scale deformation. This simulator is generated by the real-time FEM-based analysis by using a neural network. There are various deformation patterns of hollow organs by changing both its shape and volume. In this case, one network can not learn the stomach deformation with a huge number of its deformation pattern. To overcome the problem, we propose a method of constructing the simulator composed of multiple neural networks by 1)partitioning a training dataset into several subsets, and 2)selecting the data included in each subset. From our experimental results, we can conclude that our method can speed up the training process of a neural network while keeping acceptable accuracy. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

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  • Tracing commodities in indoor environments for service robotics

    Oscar Martinez Mozos, François Chollet, Kouji Murakami, Ken'Ichi Morooka, Tokuo Tsuji, Ryo Kurazume, Tsutomu Hasegawa

    IFAC Proceedings Volumes (IFAC-PapersOnline)   45 ( 22 )   71 - 76   2012

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    Daily life assistance for elderly people is one of the most promising scenarios for service robots in the near future. In particular, the go-and-fetch task will be one of the most demanding tasks in these cases. In this paper, we present an informationally structured room that supports a service robot in the task of daily object fetching. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is sent to a centralized management system which processes the data and makes it available to a service robot which is assisting people in the room. We additionally present the first steps of an intelligent framework used to maintain information about locations of commodities in our informationally structured room. This information will be used by the service robot to find objects under request. One of the main goal of our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activity of people and to reduce as much as possible the invasion of their privacy. In order to compensate this limited available sensor information our framework aims to exploit knowledge about people's activity and their interaction with objects to infer reliable information about the location of commodities. This paper presents simulated results that demonstrate the suitability of this framework to be applied to a service robotic environment equipped with limited sensors. In addition we discuss some preliminary experiments using our real environment and robot. © 2012 IFAC.

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  • ヒト脳座標アトラス作成におけるデジタル画像技術の応用

    宮城 靖, 福田 孝一, 諸岡 健一, 陳 献, 早見 武人, 岡本 剛, 砂川 賢二, 飛松 省三, 吉浦 敬

    機能的脳神経外科   49 ( 2 )   136 - 141   2010.12

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    三次元的整合性と細胞構築を両立させる、より簡便なヒト脳座標アトラスの作成法について検討した。日本人の献体脳(89歳男性)を用い、非接触型3次元デジタイザーにより脳全体の初期形状を記録した後、振動刃ミクロトームにより100μmの切片を作成した。その組織切片を染色した後、スキャナで電子化し、3Dニューロン再構築解析ソフトウェアを用いて再構築した。この方法は標本作製に伴う機械的変形が全くみられず、三次元的整合性と細胞構築を両立させ、より簡便にヒト脳アトラスを作成できた。

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  • A simple but accurate method for histological reconstruction of the large-sized brain tissue of the human that is applicable to construction of digitized brain database Reviewed

    Takaichi Fukuda, Ken'ichi Morooka, Yasushi Miyagi

    NEUROSCIENCE RESEARCH   67 ( 3 )   260 - 265   2010.7

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    Research on the human brain has undoubted significance, but our knowledge on its detailed morphology is still limited. We have developed a simple method for reconstruction of large-sized brain tissues of the human. Fixed brains were cut into blocks (maximum size 7 cm x 7 cm x 1 cm), embedded and post-fixed in gelatin just one overnight before obtaining complete serial sections with a vibrating microtome. Quality of stained materials was sufficient to create three-dimensional histological maps, where digital reconstructions from adjoining blocks could be accurately combined. The present method will facilitate both direct examination of the human brain and construction of its histological database. (C) 2010 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

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  • パーキンソン病 画像 デジタル画像処理技術を用いた脳座標アトラス作成法

    宮城 靖, 福田 孝一, 諸岡 健一, 陳 献, 早見 武人, 岡本 剛, 砂川 賢二, 飛松 省三, 吉浦 敬, 佐々木 富男

    機能的脳神経外科   49 ( 1 )   82 - 83   2010.6

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  • Real-Time Nonlinear FEM-Based Simulator for Deforming Volume Model of Soft Organ by Neural Network

    MOROOKA Ken'ichi, CHEN Xian, KURAZUME Ryo, UCHIDA Seiichi, HARA Kenji, SUNAGAWA Kenji, HASHIZUME Makoto

    The IEICE transactions on information and systems   93 ( 3 )   365 - 376   2010.3

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    本論文では、ニューラルネットワークを用いて、軟性臓器モデルの変形をシミュレートする新たな手法を提案する。提案手法は、基本的なモデルの変形(以後、変形モードと呼ぶ)の組合せに基づいて、モデルの変形を推定する。つまり、変形モードをあらかじめ非線形有限要素法で求め、臓器に加わった外力と、それに対応する変形モードの関係をニューラルネットワークで学習する。学習したニューラルネットワークは、非線形有限要素解析によりモデルの振舞いを推定することを模倣する。実験結果より、提案手法は、非線形有限要素解析とほぼ同程度の精度を保ちつつ、計算コストを大幅に削減することができた。(著者抄録)

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  • Wheelchair Robot Service on Smart Medical Facilities and its Evaluation

    IENAGA Takafumi, SENTA Yosuke, ARITA Daisaku, KIMURO Yoshihiko, HASEGAWA Tsutomu, MOROOKA Ken-ichi, KENMOCHI Hajime, TANOUE Kazuo, HASHIZUME Makoto

    JRSJ   27 ( 8 )   877 - 884   2009.10

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    This paper describes the on-demand patient service system which uses RFID tags and a wheelchair robot in medical facilities. In this system, the robot can identify a patient and obtain his/her diagnosis schedule by the RFID tag in his/her registration card. According to the schedule, the robot can carry the patient to his/her target examination room. To construct this system, we employ the idea of the intelligent environment, called "Robot Town". Many sensors are distributed in Robot Town, and robots can easily move and work by using the sensors. We executed a proving experiment with our system under the scenario that assumed an outpatient came to the unfamiliar and large medical facility. In addition, to evaluate our system, we executed the questionnaire survey to nurses. As the result of survey, we know that their expected points are an improvement of the daily work efficiency, safe transportation of patients and an improvement of an amenity level in a hospital. And, we also know that their worried points are safety of the robots, the abilities of detection to the unexpected emergency situations, costs and difficulties of maintenance of systems, and responsibilities in case of accidents.

    DOI: 10.7210/jrsj.27.877

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  • The frontal cortex is activated during learning of endoscopic procedures

    Kenoki Ohuchida, Hajime Kenmotsu, Atsuyuki Yamamoto, Kazuya Sawada, Takehito Hayami, Kenichi Morooka, Shinichiro Takasugi, Kozo Konishi, Satoshi Ieiri, Kazuo Tanoue, Yukihide Iwamoto, Masao Tanaka, Makoto Hashizume

    SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES   23 ( 10 )   2296 - 2301   2009.10

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    Background To date, several training and evaluation systems for endoscopic surgery have been developed, such as virtual-reality simulators and box trainers. However, despite current advances in these objective assessments, no functional brain studies during learning of endoscopic surgical skills have been carried out. In the present study, we investigated cortical activation using near-infrared spectroscopy (NIRS) during endoscopic surgical tasks.Study design A total of 21 right-handed subjects, comprising 4 surgical experts, 4 trainees, and 13 novices, participated in the study. Suturing and knot-tying tasks were performed in a box trainer. Cortical activation was assessed in all subjects by task-related changes in hemoglobin (Hb) oxygenation using NIRS.Results In surgical experts and novices with no experience of endoscopic surgical training, we found no changes in oxy-Hb, deoxy-Hb or total-Hb levels in any of the frontal channels. In surgical trainees and one novice with experience of endoscopic surgical training, we found significant increases in oxy-Hb and total-Hb levels in most of the frontal channels. There were significant differences in oxy-Hb and total-Hb levels in CH-19 between surgical experts and trainees (p = 0.02 for both), and between surgical trainees and novices with no experience of endoscopic surgical training (p = 0.008 for both). Furthermore, additional training increased oxy-Hb levels in the frontal cortex of novices with no experience of endoscopic surgical training but had no such effect on surgical experts.Conclusions The present data suggest that NIRS is a feasible tool for assessing brain activation during endoscopic surgical tasks, and may have a large impact on the future development of teaching, training, and assessment methods for endoscopic surgical skills.

    DOI: 10.1007/s00464-008-0316-z

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  • The frontal cortex is activated during learning of endoscopic procedures. International journal

    Kenoki Ohuchida, Hajime Kenmotsu, Atsuyuki Yamamoto, Kazuya Sawada, Takehito Hayami, Kenichi Morooka, Shinichiro Takasugi, Kozo Konishi, Satoshi Ieiri, Kazuo Tanoue, Yukihide Iwamoto, Masao Tanaka, Makoto Hashizume

    Surgical endoscopy   23 ( 10 )   2296 - 301   2009.10

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    BACKGROUND: To date, several training and evaluation systems for endoscopic surgery have been developed, such as virtual-reality simulators and box trainers. However, despite current advances in these objective assessments, no functional brain studies during learning of endoscopic surgical skills have been carried out. In the present study, we investigated cortical activation using near-infrared spectroscopy (NIRS) during endoscopic surgical tasks. STUDY DESIGN: A total of 21 right-handed subjects, comprising 4 surgical experts, 4 trainees, and 13 novices, participated in the study. Suturing and knot-tying tasks were performed in a box trainer. Cortical activation was assessed in all subjects by task-related changes in hemoglobin (Hb) oxygenation using NIRS. RESULTS: In surgical experts and novices with no experience of endoscopic surgical training, we found no changes in oxy-Hb, deoxy-Hb or total-Hb levels in any of the frontal channels. In surgical trainees and one novice with experience of endoscopic surgical training, we found significant increases in oxy-Hb and total-Hb levels in most of the frontal channels. There were significant differences in oxy-Hb and total-Hb levels in CH-19 between surgical experts and trainees (p = 0.02 for both), and between surgical trainees and novices with no experience of endoscopic surgical training (p = 0.008 for both). Furthermore, additional training increased oxy-Hb levels in the frontal cortex of novices with no experience of endoscopic surgical training but had no such effect on surgical experts. CONCLUSIONS: The present data suggest that NIRS is a feasible tool for assessing brain activation during endoscopic surgical tasks, and may have a large impact on the future development of teaching, training, and assessment methods for endoscopic surgical skills.

    DOI: 10.1007/s00464-008-0316-z

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  • The effect of CyberDome, a novel 3-dimensional dome-shaped display system, on laparoscopic procedures. International journal

    Kenoki Ohuchida, Hajime Kenmotsu, Atsuyuki Yamamoto, Kazuya Sawada, Takehito Hayami, Kenichi Morooka, Hiroshi Hoshino, Munenori Uemura, Kozo Konishi, Daisuke Yoshida, Takashi Maeda, Satoshi Ieiri, Kazuo Tanoue, Masao Tanaka, Makoto Hashizume

    International journal of computer assisted radiology and surgery   4 ( 2 )   125 - 32   2009.3

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    BACKGROUND: Laparoscopic surgeons require extended experience of cases to overcome the lack of depth perception on a two-dimensional (2D) display. Although a three-dimensional (3D) display was reported to be useful over two decades ago, 3D systems have not been widely used. Recently, we developed a novel 3D dome-shaped display (3DD) system, CyberDome. STUDY DESIGN: In the present study, a total of 23 students volunteered. We evaluated the effects of the 3DD system on depth perception and laparoscopic procedures in comparison with the 2D, a conventional 3D (3DP) or the 2D high definition (HD) systems using seven tasks. RESULTS: The 3DD system significantly improved depth perception and laparoscopic performance compared with the 2D system in six new tasks. We further found that the 3DD system shortened the execution time and reduced the number of errors during suturing and knot tying. The 3DD system also provided more depth perception than the 3DP and 2D HD systems. CONCLUSIONS: The novel 3DD system is a promising tool for providing depth perception with high resolution to laparoscopic surgeons.

    DOI: 10.1007/s11548-009-0282-5

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  • The effect of CyberDome, a novel 3-dimensional dome-shaped display system, on laparoscopic procedures

    Kenoki Ohuchida, Hajime Kenmotsu, Atsuyuki Yamamoto, Kazuya Sawada, Takehito Hayami, Kenichi Morooka, Hiroshi Hoshino, Munenori Uemura, Kozo Konishi, Daisuke Yoshida, Takashi Maeda, Satoshi Ieiri, Kazuo Tanoue, Masao Tanaka, Makoto Hashizume

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   4 ( 2 )   125 - 132   2009.3

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    Background Laparoscopic surgeons require extended experience of cases to overcome the lack of depth perception on a two-dimensional (2D) display. Although a three-dimensional (3D) display was reported to be useful over two decades ago, 3D systems have not been widely used. Recently, we developed a novel 3D dome-shaped display (3DD) system, CyberDome.Study design In the present study, a total of 23 students volunteered. We evaluated the effects of the 3DD system on depth perception and laparoscopic procedures in comparison with the 2D, a conventional 3D (3DP) or the 2D high definition (HD) systems using seven tasks.Results The 3DD system significantly improved depth perception and laparoscopic performance compared with the 2D system in six new tasks. We further found that the 3DD system shortened the execution time and reduced the number of errors during suturing and knot tying. The 3DD system also provided more depth perception than the 3DP and 2D HD systems.Conclusions The novel 3DD system is a promising tool for providing depth perception with high resolution to laparoscopic surgeons.

    DOI: 10.1007/s11548-009-0282-5

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  • Real-Time Nonlinear FEM with Neural Network for Simulating Soft Organ Model Deformation Reviewed

    Ken'ichi Morooka, Xin Chen, Ryo Kurazume, Seiichi Uchida, Kenji Hara, Yumi Iwashita, Makoto Hashizume

    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2008, PT II, PROCEEDINGS   5242 ( Pt 2 )   742 - 749   2008

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    This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. [2] that deformed model can be estimated from the superposition of basic deformation modes. The neural network finds a relationship between external forces and the models deformed by the forces. The experimental results show that the trained network can achieve a real-time simulation while keeping the acceptable accuracy compared with the nonlinear FEM computation.

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  • Fast 3D reconstruction of human shape and motion tracking by Parallel Fast Level Set Method Reviewed

    Yumi Iwashita, Ryo Kurazume, Kenji Hara, Seiichi Uchida, Ken'ichi Morooka, Tsutomu Hasegawa

    2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-9   980 - +   2008

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    This paper presents a parallel algorithm of the Level Set Method named the Parallel Fast Level Set Method, and its application for real-time 3D reconstruction of human shape and motion. The Fast Level Set Method is an efficient implementation algorithm of the Level Set Method and has been applied to several applications such as object tracking in video images and 3D shape reconstruction using multiple stereo cameras. In this paper, we implement the Fast Level Set Method on a PC cluster and develop a real-time motion capture system for arbitrary viewpoint image synthesis. To obtain high performance on a PC cluster, efficient load-balancing and resource allocation algorithms are crucial problems. We develop a novel optimization technique of load distribution based on the estimation of moving direction of object boundaries. In this technique, the boundary motion is estimated in the framework of the Fast Level Set Method, and the optimum load distribution is predicted and performed according to the estimated boundary motion and the current load balance. Experiments of human shape reconstruction and arbitrary viewpoint image synthesis using the proposed system are successfully carried out.

    DOI: 10.1109/ROBOT.2008.4543332

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  • Self-organizing deformable model for mapping 3D object model onto arbitrary target surface Reviewed

    Ken'ichi Morooka, Shun Matsui, Hiroshi Nagahashi

    3DIM 2007: SIXTH INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS   193 - +   2007

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    This paper presents a new technique for projecting a 3D object mesh model onto a surface of another target object. The mesh model adapts its shape to the target surface, and is called Self-organizing Deformable Model(SDM). The SDM algorithm works by combining a competitive learning and an energy minimization. The framework of the SDM makes it possible to map a mesh model onto various kinds of target surfaces. This characteristic can not be seen in other methods for surface parameterization, and it enables us to apply the SDM to some different fields in computer vision and computer graphics. Also the SDM can reconstruct shapes of target objects similar to general deformable models.

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  • A method for integrating range images with different resolutions for 3-D model construction Reviewed

    Ken'ichi Morooka, Hiroshi Nagahashi

    2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10   3070 - +   2006

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    This paper proposes a new method for integrating range images with different resolutions to generate a whole surface model of a 3D object. Generally, a modeling method using range images needs an overlapping area between the images for the registration process. Accordingly, there are redundant data in the overlapping area. In order to produce a seamless and non-redundant model, we integrate the data, and represent the overlapping area with triangular patches. However, few conventional methods have paid attention to integrating images with different resolutions. We propose a new integration method in which geometric and topological information are used. Experimental results show that our proposed method is efficient for integrating multiple range images.

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  • Texture classification using hierarchical linear discriminant space Reviewed

    YS Kang, K Morooka, H Nagahashi

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E88D ( 10 )   2380 - 2388   2005.10

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    As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. In order to take such overlaps among classes into consideration, our approach builds a new discriminant space by hierarchically classifying the overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We divide the discriminant space into subspaces by recursively grouping the overlapped classes. In the experiment, texture images from many classes are classified based on the proposed method. We show the outstanding result compared with the conventional Fisher method.

    DOI: 10.1093/ietisy/e88-d.10.2380

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  • Self-organizing deformable model: A new method for fitting mesh model to given object surface Reviewed

    K Morooka, H Nagahashi

    ADVANCES IN VISUAL COMPUTING, PROCEEDINGS   3804   151 - 158   2005

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    This paper presents a new method for projecting a mesh model of a source object onto a surface of an arbitrary target object. A deformable model, called Self-organizing Deformable Model(SDM), is deformed so that the shape of the model is fitted to the target object. We introduce an idea of combining a competitive learning and an energy minimization into the SDM deformation. Our method is a powerful tool in the areas of computer vision and computer graphics. For example, it enables to map mesh models onto various kinds of target surfaces like other methods for a surface parameterization, which have focused on specified target surface. Also the SDM can reconstruct shapes of target objects like general deformable models.

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  • Scale invariant texture analysis using multi-scale local autocorrelation features Reviewed

    Y Kang, K Morooka, H Nagahashi

    SCALE SPACE AND PDE METHODS IN COMPUTER VISION, PROCEEDINGS   3459   363 - 373   2005

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    We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multiscale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis.

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  • A method for watermarking to Bezier polynomial surface models Reviewed

    H Nagahashi, R Mitsuhashi, K Morooka

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E87D ( 1 )   224 - 232   2004.1

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    This paper presents a new method for embedding digital watermarks into Bezier polynomial patches. An object surface is supposed to be represented by multiple piecewise Bezier polynomial patches. A Bezier patch passes through its four-corner control points, which are called data points, and does not pass through the other control points. To embed a watermark, a Bezier patch is divided into two patches. Since each subdivided patch shares two data points of the original patch, the subdivision apparently generates two additional data points on the boundaries of the original patch. We can generate the new data points in any position on the boundaries by changing the subdivision parameters. The additional data points can not be removed without knowing some parameters for subdividing and deforming the patch, hence the patch subdivision enables us to embed a watermark into the surface.

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  • Texture classification using hierarchical discriminant analysis Reviewed

    S Yasuoka, Y Kang, K Morooka, H Nagahashi

    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7   6395 - 6400   2004

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    As the representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to multi-class classification problem, the precision of its discrimination may become worse. One of the main reasons is an occurence of overlapped distributions on a discriminant space built by Fisher criterion. In order to take such overlap among classes into consideration, our approach builds a new discriminant space with hierarchical tree structure for overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We can divide a discriminant space into subspace by recursively grouping overlapped classes. In the experiment, texture images of many classes are classified based on the proposed method, and we show the outstanding result compared with the conventional method.

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  • 3-dimensional object model construction from range images taken by a range finder on a mobile robot Reviewed

    N Okada, HB Zha, T Nagata, E Kondo, K Morooka

    1998 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS - PROCEEDINGS, VOLS 1-3   1853 - 1858   1998

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    To construct a 3D model of an object in a computer, a range finder is used to obtain range images of the object. The range finder takes range images from many view points to eliminate the influence of occlusions. A registration algorithm is, then, used to merge the range images.
    When the range finder is mounted on a mobile robot and measures while moving, range images taken by it will be distorted. We extended the registration algorithm to remove the deformation. The extended algorithm can remove it and is used to construct a SD model even for a case where the speed parameters of the robot are not well known.

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Books

  • Multidisciplinary computational anatomy : toward integration of artificial intelligence with MCA-based medicine(A Computer-Aided Support System for Deep Brain Stimulation by Multidisciplinary Brain Atlas Database)

    Hashizume Makoto, Ken’ichi Morooka, Shoko Miyauchi, Yasushi Miyagi

    Springer  2022  ( ISBN:9789811643248

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    Total pages:xv, 398 p.   Language:English

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  • 放射線治療AIと外科治療AI

    有村秀孝, 諸岡健一

    オーム社  2020.4  ( ISBN:9784274225475

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    Total pages:xi, 217p   Language:Japanese

    CiNii Books

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  • 医用画像ディープラーニング入門

    藤田, 広志(Chapter 18: 諸岡健一)

    オーム社  2019.4  ( ISBN:9784274223655

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    Total pages:xi, 210p   Language:Japanese

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MISC

  • 3次元心臓モデルための動作補間手法の構築

    チャン チュジェ, 宮内 翔子, 諸岡 健一, 倉爪 亮

    情報処理学会 コンピュータビジョンとイメージメディア研究会   2022.11

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  • 陰関数表現を用いた同一構造を持つ3次元物体メッシュモデル生成法の構築

    板谷 響, 宮内 翔子, 諸岡 健一

    情報処理学会コンピュータビジョンとイメージメディア研究会   2022.11

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  • 深層学習によるNBI内視鏡画像を用いた上部消化管腫瘍抽出

    李澤昊, 諸岡健一, 江端由穂, 蓮田博文, 宮内翔子, 太田光彦

    電子情報通信学会医用画像研究会   2022.9

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  • 患者の心臓動的形状とメタデータを用いた虚血性心疾患診断システムの構築

    宮内翔子, 諸岡健一, 倉爪亮

    電子情報通信学会医用画像研究会   2022.7

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  • Multiple Instance Learningによる大腸病理画像からの癌再発予測システムの構築

    大森一輝, 諸岡健一, 中西良太, 宮内翔子, 沖英次, 吉住朋晴

    電子情報通信学会医用画像研究会   2022.5

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  • 多重焦点画像列を用いたOptical Projection Tomographyの DNN用ライブラリ実装

    石井 直行, 長原 一, 諸岡 健一

    第229回CVIM研究発表会   2022.3

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  • A Computer-Aided Support System for Deep Brain Stimulation by Multidisciplinary Brain Atlas Database

    Ken’ichi Morooka, Shoko Miyauchi, Yasushi Miyagi

    Multidisciplinary Computational Anatomy   163 ( 167 )   163 - 167   2022

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    Authorship:Lead author   Language:English   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:Springer Singapore  

    DOI: 10.1007/978-981-16-4325-5_20

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  • 画像認識による未固定大腸癌切除標本中の病変部検出と腫瘍正常識別および深達度予測

    寅田信博, 大内田研宙, 諸岡健一, 永井俊太郎, 水内祐介, 河田純, 小田義直, 中村雅史

    JDDW   2021.11

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  • Dynamic Shape Feature Extraction for Heart using Ladder Variational Autoencoder

    TIAN Weiye, 宮内翔子, 倉爪亮, 諸岡健一

    計測自動制御学会九州支部学術講演会予稿集(CD-ROM)   40th   2021.11

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  • 同一構造を持つ3次元物体メッシュモデルの識別に適したGraph Convolutional Network構造の検討

    板谷響, 宮内翔子, 諸岡健一

    画像の認識・理解シンポジウムMIRU2021   2021.7

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  • 触媒ナノ粒子の画像解析における転移学習の有効性の評価

    小山 朗, 宮内 翔子, 諸岡 健一, 北條 元, 永長 久寛, 村上 恭和

    日本顕微鏡学会 第77回学術講演会   2021.6

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  • 骨格形状への機械学習の応用

    諸岡健一, 宮内翔子

    第41回日本骨形態計測学会記録集   80 - 81   2021.6

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  • AIと手術支援 Invited

    諸岡健一

    小児外科   53 ( 4 )   417 - 420   2021.4

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  • Graph Convolutional Networkによる口唇口蓋裂患者の咬合評価

    宮内翔子, 渡邉匠吾, 板谷 響, 谷川千尋, 谷村百和子, 山城 隆, 長原 一, 諸岡健一

    電子情報通信学会技術研究報告   MI2020-51   21 - 21   2021.3

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  • 画像診断が医療現場を変える: 3. 外科治療AI Invited

    諸岡健一

    情報処理学会誌   62 ( 2 )   e14 - e18   2021.2

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  • 転移学習を用いた触媒ナノ粒子の電子顕微鏡画像の解析

    小山 朗, 宮内 翔子, 太田 潤, 諸岡 健一, 高橋 由夫, 谷垣 俊明, 品田 博之, 北條 元, 永長 久寛, 村上 恭和

    日本金属学会 第167回講演大会   2020.9

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  • 転移学習を用いた畳み込みニューラルネットワークによる触媒ナノ粒子の画像解析

    小山 朗, 太田 潤, 宮内 翔子, 諸岡 健一, 北條 元, 永長 久寛, 中島 宏, 村上 恭和

    日本顕微鏡学会 第76回学術講演会   2020.5

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  • 日用品形状と人動作の統計的学習に基づくアーム動作生成

    田島翔, 川久保淳志, 辻徳生, 鈴木陽介, 渡辺哲陽, 宮内翔子, 諸岡健一, 原田研介, 関啓明

    ロボティクス・メカトロニクス講演会講演概要集   2A1-M17   2020.5

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  • 3D Brain Atlas Construction from Single Japanese Cadaver Brain

    野田陽太, 諸岡健一, 宮城靖, 福田孝一, 宮内翔子, 倉爪亮

    電子情報通信学会技術研究報告   119 ( 399(MI2019 65-123)(Web) )   2020

  • 3D Cell Shape Reconstruction From Multifocal Microscope Image Sequence

    山口貴大, 長原一, 諸岡健一, 中島悠太, 浦西友樹, 倉爪亮, 大野英治

    電子情報通信学会技術研究報告   118 ( 405 )   173 - 179   2019.1

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  • Tri-Scan強調画像を用いたU-Netによる膀胱鏡画像からの腫瘍検出

    楳原愛子, 諸岡健一, 牟田口淳, 小林聡, 宮内翔子, 倉爪亮, 江藤正俊

    日本コンピュータ外科学会誌   21 ( 4 (Web) )   2019

  • Gaussian Process Dynamical Modelを用いた多元心臓統計的形状モデルの構築

    内林光優, 宮内翔子, 諸岡健一, DUAN Jinming, BAI Wenjia, RUECKERT Daniel, 倉爪亮

    電子情報通信学会技術研究報告   119 ( 263(MICT2019 23-37)(Web) )   2019

  • 深層学習と術具3次元形状モデルの組み合わせによるロボット支援内視鏡手術画像からの術具位置姿勢推定

    堤田有美, 諸岡健一, 小林聡, 宮内翔子, 江藤正俊, 倉爪亮

    日本コンピュータ外科学会誌   20 ( 4 )   258   2018.10

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  • 脊椎側彎症治療診断支援システムのための深層学習による脊椎変形・応力分布推定

    大山紗貴子, 諸岡健一, 小林薫樹, 久保田健介, 倉爪亮

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)   18th   2017

  • modified Self-organizing Deformable Modelを用いた臓器表面モデルに対する幾何特徴量保存写像の高速化

    宮内翔子, 諸岡健一, 宮城靖, 福田孝一, 倉爪亮

    電子情報通信学会技術研究報告   117 ( 220(MI2017 37-46) )   2017

  • A Method for Mapping Organ Volume Model with Internal Structure onto Target Volume

    116 ( 160 )   49 - 54   2016.7

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  • Vertebra Shape Reconstruction from Low Quality Medical Images Using Statistical Shape Model and Particle Filtering

    藤崎祥平, 諸岡健一, 久保田健介, 宮内翔子, 辻徳生, 倉爪亮

    電子情報通信学会技術研究報告   115 ( 401(MI2015 73-145) )   2016

  • 複数の遠赤外線画像を用いた全周の接触領域検出と把持形態推定への応用

    稲田大亮, 辻徳生, 諸岡健一, 田原健二, 河村晃宏, 倉爪亮, 原田研介

    日本ロボット学会学術講演会予稿集(CD-ROM)   34th   2016

  • 統計的形状モデルを用いた古人骨の骨盤形状復元

    諸岡健一, 松原良太, 宮内翔子, 福田孝一, 杉井健, 倉爪亮

    情報科学技術フォーラム講演論文集   15th   2016

  • ボトックス筋肉内注射ナビゲーションのための重畳表示システムの構築

    牛垣雅人, 諸岡健一, 宮城靖, 倉爪亮

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)   17th   2016

  • Tissue Volume Model Mapping onto Target Volume Based on modified Self-organizing Deformable Model

    宮内翔子, 諸岡健一, 辻徳生, 宮城靖, 福田孝一, 倉爪亮

    電子情報通信学会技術研究報告   115 ( 401(MI2015 73-145) )   2016

  • Non-Rigid Registration of Brain Block Models for Constructing 3D Japanese Brain Atals

    佐々木翔, 諸岡健一, 宮内翔子, 辻徳生, 宮城靖, 福田孝一, 佐村和宏, 倉爪亮

    電子情報通信学会技術研究報告   115 ( 401(MI2015 73-145) )   2016

  • Estimation of Liver Deformation Using Real-time Nonlinear Finite Element Method by Deep Neural Network

    小林薫樹, 諸岡健一, 宮城靖, 福田孝一, 辻徳生, 倉爪亮, 左村和宏

    電子情報通信学会技術研究報告   115 ( 401(MI2015 73-145) )   2016

  • 1A1-N01 Task management system for informationally structured architecture ROS-TMS : Structured task information management experiments for a variety of robots

    HASHIGUCHI Yuuka, PYO Yoonseok, TSUJI Tokuo, MOROOKA Ken'ichi, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2015 ( 0 )   _1A1 - N01_1-_1A1-N01_4   2015

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    This paper presents a framework of a task management system for the informationally structured architecture, ROS-TAM, and information structured task management experiments using different types of robots and service tasks. The proposed system interprets user's request, plans a proper robot service, issues a series of robot commands suitable for the structure of each robot, and executes the robot service by task executing machine, SMACH. We conducted a go-and-fetch task with two types of robots.

    DOI: 10.1299/jsmermd.2015._1A1-N01_1

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  • 1A1-H05 Development of small sensor terminal "Portable" and automatic reconfiguration for pedestrian tracking

    WATANABE Yuuta, KURAZUME Ryo, Pyo Yoonseok, TSUJI Tokuo, MOROOKA Ken'ichi

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2015 ( 0 )   _1A1 - H05_1-_1A1-H05_4   2015

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    This paper proposes a small, lightweight, and easily-relocated sensor terminal named "Portable" for acquiring a variety of environmental information. The Portable is equipped with a variety of sensors including a pyroelectric sensor, a proximity sensor, a sound pressure sensor, a thermometer, a hygrometer, a gas sensor, a flame sensor, and a laser range finder. We introduce three typical applications: abnormality detection, pedestrian tracking and automatic reconfiguration of several Portables for pedestrian tracking.

    DOI: 10.1299/jsmermd.2015._1A1-H05_1

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  • 2A1-T05 Active Object Recognition Using Depth Sensor

    NAKAZATO Kazuki, MOROOKA Ken'ichi, MIYAUCHI Syoko, TSUJI Tokuo, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2015 ( 0 )   _2A1 - T05_1-_2A1-T05_4   2015

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    This paper presents a viewpoint planning method for an active object recognition by using the shape information of objects. The proposed method uses the certainty that a given object is classifed into one category. The certainty is represented by a probabilistic model. Moreover, the categorization possibility of unknown objects are quantied by the entropy indicating the ambiguity of the recognition. When an object is observed, the certainty is calculated by using the measured data. For each viewpoint, the entropy is computed based on the certainty. We choose as a next viewpoint the viewpoint with minimum entropy. From the experimental results using real objects, the proposed method can achieve en efficient object recognition compared with the method which selects viewpoints randomly.

    DOI: 10.1299/jsmermd.2015._2A1-T05_1

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  • 2P1-G05 Fast Planning for Safe Motion Path by using Bounding Volumes

    TSUJI Tokuo, Harada Kensuke, MOROOKA Ken'ichi, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2015 ( 0 )   _2P1 - G05_1-_2P1-G05_2   2015

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    This paper reports a new shortcut method of the path planning for keeping clearance between the path and the obstacles. The bonding volumes which have the clearance to each robot link are generated and used for estimating the rough distance of the robot and the obstacles. The effectiveness of the method is verified in simulation.

    DOI: 10.1299/jsmermd.2015._2P1-G05_1

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  • Finite Element Modelling using Hexahedral Elements Based on Self-organizing Deformable Model

    Morooka Ken'ichi, Ji Yuanting, Miyauchi Shoko, Tsuji Tokuo, Kurazume Ryo

    Transactions of Japanese Society for Medical and Biological Engineering   53 ( 0 )   S253_03 - S253_03   2015

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    Finite element method (FEM) is a well-known technique for modeling object physical behaviors. The FE analysis uses polyhedral mesh model, called FE model, of the object to be analyzed. The accuracy of the FE analysis depends on the quality of the polyhedral elements of the FE model. Here, theoretically, the FE analysis using hexahedral elements is superior to that of tetrahedral elements. However, hexahedral element construction needs to be satisfied with more constraints simultaneously compared with the tetrahedral element. Owing to the reason, there are few methods for generating hexahedral FE model of objects with complex shapes. This paper presents an automatic method for generating hexahedral FE model of human tissues. The proposed method is based on a Growing Self-organizing Deformable Model (GSDM) that is a deformable volumetric model. Practically, give a tissue surface model, we use as the initial GSDM the hexahedral model of the cuboid converted with the tissue. The hexahedral FE model is obtained by deforming the GSDM to fit the GSDM to the tissue surface and improve the quality of each element shape.

    DOI: 10.11239/jsmbe.53.S253_03

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  • 1A1-W04 Indoor Human Behavior Estimation by combining Hierarchical Hidden Markov Model and Laser Sensing System

    SUGINOHARA Kazuya, MOROOKA Ken'ichi, TSUJI Tokuo, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2015 ( 0 )   _1A1 - W04_1-_1A1-W04_4   2015

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    This paper presents a system for estimating indoor human behavior using laser range finders on the floor. The proposed method uses hierarchical hidden Markov model(H-HMM) composed of an action estimate layer and a behavior estimate layer. The former is constructed by two kinds of HMMs: one is the HMM for estimating each action, and the other is the HMM for deciding the human action considering the action continuity. In the latter layer, one HMM learns each behavior by using as the features the relative relationship among the actions and the furniture. Our behavior estimation using such features enable to recognize the behaviors robustly even thought the indoor environment is changed.

    DOI: 10.1299/jsmermd.2015._1A1-W04_1

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  • 柔軟指先と関節変位によるポテンシャル場の許容外力エネルギーに基づく把持安定性評価

    辻徳生, 馬場恒星, 田原健二, 原田研介, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   33rd   2015

  • 高速3次元距離センサによる計測点群とのリアルタイム干渉判定に基づくオンライン回避動作の生成

    稲田大亮, 辻徳生, 倉爪亮, 諸岡健一

    日本ロボット学会学術講演会予稿集(CD-ROM)   33rd   2015

  • RGB-Dカメラと高速カメラの組み合わせによる手の動きの3次元形状計測

    GUO Yuxin, 諸岡健一, 辻徳生, 能登裕子, 原田博子, 倉爪亮

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)   16th   2015

  • 統計的形状モデルを用いた把持計画

    太田悠介, 辻徳生, 宮内翔子, 諸岡健一, 田原健二, 河村晃宏, 原田研介, 倉爪亮

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)   16th   2015

  • 遠赤外線画像の熱痕跡を用いた接触履歴の検出と把持形態推定への応用

    稲田大亮, 辻徳生, 原田研介, 田原健二, 河村晃宏, 諸岡健一, 倉爪亮

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)   16th   2015

  • プライバシーの完全保護を実現する匿名カメラの提案と異常行動検出実験

    TAKAGI SHUHEI, NAGAHARA HAJIME, IWASHITA YUMI, TSUJI TOKUO, MOROOKA KEN'ICHI, KURAZUME RYO

    日本ロボット学会学術講演会予稿集(CD-ROM)   32nd   ROMBUNNO.2J2-01   2014.9

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  • 幾何特徴量を用いた視点計画に基づく物体認識

    NAKAZATO KAZUKI, MOROOKA KEN'ICHI, TSUJI TOKUO, KURAZUME RYO

    日本ロボット学会学術講演会予稿集(CD-ROM)   32nd   ROMBUNNO.1J1-05   2014.9

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  • 情報構造化アーキテクチャの提案とサービスロボットのオンライン動作計画の実現

    PYO YOONSEOK, TSUJI TOKUO, HASHIGUCHI YUKA, NAGATA AKIHIRO, NAKAJIMA KOHEI, KURAZUME RYO, HASEGAWA TSUTOMU, MOROOKA KEN'ICHI

    ロボティクスシンポジア予稿集   19th   624 - 630   2014.3

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  • D-16-6 Shape Reconstruction from Stereo Endoscopic Images Using Wide-Range Edge

    Nakasuka Yosuke, Morooka Ken'ichi, Tuji Tokuo, Kurazume Ryo

    Proceedings of the IEICE General Conference   2014 ( 2 )   170 - 170   2014.3

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  • A Method for Mapping Tissue Surface Model onto Target Surface Based on Self-Organizing Deformable Model Preserving Geometrical Features

    MIYAUCHI Shoko, MOROOKA Ken'ichi, MIYAGI Yasushi, FUKUDA Takaichi, TSUJI Tokuo, KURAZUME Ryo

    The IEICE transactions on information and systems (Japanese edition)   97 ( 3 )   381 - 392   2014.3

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    複雑な形状をもつ人体組織同士を比較する際,それぞれのメッシュモデルを,形状が単純な目標曲面にいったん写像して,写像先で差異を比較する手法がある.このとき,対象組織で共通の解剖学的特徴が,目標曲面上で同じ位置にあると,他の部位でも対応付けが容易になる.また,組織形状に近い曲面を目標曲面として選ぶことで,写像が単純で直感的になり,解析しやすくなる.しかし,従来の写像法では,写像先を直接的に制御できず,また,従来法の目標曲面は平面や球面のみであり,形状を自由に設定するのは困難である.そこで,本論文では,特に脳表モデルに対し,モデルの写像先を制御しながら,脳表に適した形状の目標曲面へ写像する新たな手法を提案する.まず,自己組織化可変モデル変形法を用いて,モデルを目標曲面上へ写像する.この変形法を用いることで,写像の直接的制御や,モデルと同一位相をもつ形状の目標曲面が使用可能となる.この際,隣接していないモデルの頂点が,目標曲面上で同じ位置に写像されている折り畳みが生じている場合があり,この折り畳みを除去する.次に,モデルの幾何情報の一つである,モデルの表面積に対する各パッチの面積比を写像前後で保存しつつ,目標曲面に脳表を写像する.6個の脳表モデルを用いた実験を行い,提案手法は,特徴領域を特定の位置に写像しつつ,脳表モデルを目標曲面へ滑らかに写像できることを確認した.

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  • Hexahedral Finite Element Modeling of Human Tissue by Using Growing Self-Organizing Deformable Model

    OSHIBUCHI Mari, MOROOKA Ken'ichi, TSUJI Tokuo, KURAZUME Ryo

    IEICE technical report.   113 ( 410 )   149 - 154   2014.1

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    Finite Element Method (FEM) is one of the techniques for deformation simulations of human tissue. The basic concept of FEM is the discretization of an object into FE-model that consists of simple geometry called elements. Although The most common type of elements are tetrahedral elements, Hexahedral elements have an advantage of analysis accuracy. However, automatic generation method of hexahedral elements for complex shape have not been established. This paper proposes a method that generate generate hexahedral finite element model for complex shape using Growing Self-Organizing Deformable Model (GSDM). By several simulations using our proposed model, we confirmed that deformation analysis is performed with higher accuracy and in a shorter time.

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  • Area- and Angle-preserving Projection of Tissue Surface Model onto Target Surface

    MIYAUCHI SHOKO, MOROOKA KEN'ICHI, TSUJI TOKUO, MIYAGI YASUSHI, FUKUDA TAKAICHI, KURAZUME RYO

    電子情報通信学会技術研究報告   113 ( 410(MI2013 56-125) )   143 - 148   2014.1

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  • Automatic Estimation of Distribution of Cone Cells to Vessel Locations Using Retina Image

    JI Yuanting, MOROOKA Ken'ichi, MARTINEZ MOZOS Oscar, TSUJI Tokuo, KURAZUME Ryo

    IEICE technical report.   113 ( 410 )   281 - 284   2014.1

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    This paper proposes a method to automatically estimate the distribution patterns of cone cells in a given retina image. As to the spatial relationship between cones and vessels in the retina image, there are three types of the distribution patterns: positive correlation distribution (PCD), negative correlation distribution (NCD), and random distribution (RD). PCD and NCD indicate that the cones tend to be close to or far from the vessels. While the cone cells do not have significant correlation with vessels, the cone distribution is regarded as RD. In our method, a sample image with RD is generated by the vessels extracted from the image, and the virtual cells are selected randomly from the image. Repeating the selection process, many sample images with RDare used to estimate the distribution range of RD. When the distribution of the original cells is above the upper limit or below the lower limit of the RD distribution, the cell distribution is NCD or PCD. Otherwise, the cell distribution is regarded as RD.

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  • 1P2-H01 Tracking Persons using Laser Range Finder and Accelerometer Attached to the footwear(Integrating Ambient Intelligence)

    TSUJI Tokuo, KUSAKA Kazuya, HASEGAWA Tsutomu, KURAZUME Ryo, MOROOKA Ken'ichi

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2014 ( 0 )   _1P2 - H01_1-_1P2-H01_4   2014

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    This paper describes a method for tracking identified persons in a room using the accelerometer attached to each footwear and the laser range finder (LRF) placed on the floor. Even though the LRF measures positions of foot on the floor, it is difficult to identify each person since the floor sensor acquires only the outline of foot. In order to identify the foot, the accelerometer is attached to the footwear. When the feet contacts the floor, it starts to be measured as a blob by the LRF and the acceleration becomes constant. The system detects the timing of such state changes and finds the correspondence between blobs and accelerometers.

    DOI: 10.1299/jsmermd.2014._1P2-H01_1

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  • 1P2-H02 Robot service architecture based on room condition estimation by distributed sensors(Integrating Ambient Intelligence)

    Pyo Yoonseok, Nagata Akihiro, Nakashima Kouhei, Kuwahata Shunya, Tsuji Tokuo, Morooka Ken'ichi, Kurazume Ryo, Hasegawa Tsutomu

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2014 ( 0 )   _1P2 - H02_1-_1P2-H02_4   2014

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    This paper introduces an information processing architecture named ROS-TMS for informationally structured environment. This architecture enables to handle several practical service tasks such as state estimation in a room using accumulated information in a database, and automatic planning and execution for suitable service tasks. As an application of the proposed ROS-TMS, we present an emergency detection and alert system using distributed sensors and a service robot.

    DOI: 10.1299/jsmermd.2014._1P2-H02_1

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  • 3P2-R01 Grasp Planning for a Multi-fingered Hand with Nails(Robot Hand Mechanism and Grasping Strategy (2))

    Baba Kosei, Tsuji Tokuo, Pyo Yoonseok, Kurazume Ryo, Morooka Ken'ichi, Hasegawa Tsutomu, Harada Kensuke

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2014 ( 0 )   _3P2 - R01_1-_3P2-R01_4   2014

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    We aim to develop a multi-fingered hand which grasps various objects. We develop a finger equipped with soft fingertip and two layers nails. The first layer of the nails is thin and long. It is possible to insert it into the bottom of the object. The second layer of the nails is thick and short. It supports the elastic force of soft fingertip. We develop a planner for selecting a grasp style according to the height of the object. The planner can grasp a low height object by using a multi-fingered hand. We demonstrate the feasibility of the proposed method with simulation. In addition, it is shown that the developed hand can grasp various objects through experimental results.

    DOI: 10.1299/jsmermd.2014._3P2-R01_1

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  • 柔軟指先と把持物体のポテンシャルを用いた把持安定条件の導出

    馬場恒星, 辻徳生, 田原健二, 原田研介, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   32nd   2014

  • Measurement of Moving Objects and Estimation of Human Behavior Using Floor Sensing System in Daily Life Environment

    HASEGAWA Tsutomu, PYO Yoonseok, TANAKA Masahide, TSUJI Tokuo, MOROOKA Ken'ichi, KURAZUME Ryo

    Journal of the Robotics Society of Japan   31 ( 8 )   769 - 779   2013.10

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    This paper describes a method of measuring moving objects and estimating human behaviors in a room using only one laser range finder (LRF) installed in the room and a strip of mirror attached to a side wall close to a floor. The area of sensing is limited to a plane parallel to and just a few centimeters above the floor, thus covering the whole room with minimal invasion of privacy of a resident while reducing occlusion. The important feature of the measurement consists in processing of both distance and reflectance acquired by the LRF from the surface of the existing objects. This enables immediate distinction of clusters of objects made of different materials in the analysis of the scene cluttered with objects. The human behavior models are effectively utilized to estimate human behavior from LRF data. The experimental results validate the effectiveness of the proposed method.

    DOI: 10.7210/jrsj.31.769

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  • ABNORMAL BEHAVIOR DETECTION USING SURVEILLANCE VIDEOS

    Takaki Shuhei, Iwashita Yumi, Morooka Kenichi, Tsuji Tokuo, Kurazume Ryo

    Proceedings of the Society Conference of IEICE   2013   "S - 50"-"S-51"   2013.9

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  • Mapping of Brain Surface Mesh Model onto Arbitrary Closed Surface

    MIYAUCHI Shoko, MOROOKA Ken'ichi, MIYAGI Yasushi, FUKUDA Takaichi, TSUJI Tokuo, KURAZUME Ryo

    IEICE technical report.   113 ( 146 )   39 - 44   2013.7

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    This paper proposes a new method for mapping tissue surface model onto arbitrary target surface whilepreserving the geometrical features of the tissue surface. In our method, firstly, the tissue model is roughly deformed by using Self-organizing deformable model. Since the deformed model may contain folded patches, the folded patches are removed. Moreover, FFD and area-preserving mapping are used to map the model onto the target surface while keeping the ratio of the area of each patch to the area of of the whole tissue surface. From several experimental results, we can conclude that the proposed method can map tissue models onto arbitrary target surface without foldovers.

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  • A Method for Selecting Landmark to Generate Patient-Specific Brain Atlas by Deforming Standard Brain Atlas

    KOBAYASHI Kaoru, MOROOKA Ken'ichi, MIYAGI Yasushi, FUKUDA Takaichi, TSUJI Tokuo, KURAZUME Ryo

    Transactions of Japanese Society for Medical and Biological Engineering   51 ( 6 )   390 - 396   2013

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    To estimate the internal structures of the patient brain, several methods map a brain atlas to a patient brain shape using some landmarks, which are selected manually from the brain shape. The determination of the correspondence between small sulci of arbitrary two brains is complex. Moreover, the relationship between the surface shape and internal structure of the brain is unclear. Therefore, even if the surface the deformed atlas is fitted to the patient brain shape, the accurate internal structures of the brain can not always be obtained. To solve these problems, this paper proposes a method of selecting landmarks to estimate the internal structures of the patient brain by deforming brain atlas. Firstly, the brain shapes are represented by a simple shape. Secondly, a small number of initial landmarks are selected manually from the contours of the approximated brain shape and the internal structures which can be identified from MR images. Thirdly, some new landmarks are generated automatically on the contours based on the initial landmarks. Finally, the brain atlas is deformed non-rigidly using these landmarks. To evaluate our method, we estimate the patient brain structure using 4 methods. From the experimental results using 10 brain images, our method can estimate the patient brain structure reliably and stably using a small number of landmarks compared with the method using the landmarks on the original brain shape.

    DOI: 10.11239/jsmbe.51.390

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  • 2A2-Q08 Development of a personal cleaning robot to collect everyday objects on the floor(Robots for Home/Office Application)

    HASHIGUCHI Yuuka, HASEGAWA Tsutomu, PYO Yoonseok, TSUJI Tokuo, MOROOKA Ken'ichi, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2013 ( 0 )   _2A2 - Q08_1-_2A2-Q08_4   2013

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    We developed a service robot that picks up everyday objects. The robot is equipped with a small manipulator and a Kinect sensor. A role of the robot is to collect objects which lie scattered on the floor of a room before a cleaning robot (Roomba) works. The robot uses not only a sensor mounted on it but also one laser range finder (LRF) installed in the room. By this, the robot can find objects and move to the grasping point efficiently.

    DOI: 10.1299/jsmermd.2013._2A2-Q08_1

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  • 生活支援のための環境分散センサ情報統合アーキテクチャ

    永田晃洋, 長谷川勉, 表允せき, 辻徳生, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   31st   2013

  • 情報構造化環境における日用品の追跡-3次元ポイントクラウドを用いた変化箇所の検出と物体識別-

    CHAE Hyunk, 桑畑舜也, MARTINEZ MOZOS Oscar, 長谷川勉, 辻徳生, 諸岡健一, 倉爪亮

    ロボティクスシンポジア予稿集   18th   2013

  • 床上センシングシステムと室内生活行動モデルにもとづく居住者の行動推定

    長谷川勉, 田中真英, PYO Yoonseok, 辻徳生, 諸岡健一, 倉爪亮

    ロボティクスシンポジア予稿集   18th   2013

  • 情報構造化環境における家具上物品検出のための移動ロボットによる視覚記憶照合と変化検出

    桑畑舜也, 長谷川勉, 諸岡健一, 倉爪亮, 辻徳生

    日本ロボット学会学術講演会予稿集(CD-ROM)   31st   2013

  • 1A1-R04 Localization of Moving Objects with Inertial Sensor and Laser Range Finder(Integrating Ambient Intelligence(1))

    MORI Tatsunori, TANAKA Masahide, TSUJI Tokuo, MURAKAMI Kouji, HASEGAWA Tsutomu, MOROOKA Kenichi, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2012 ( 0 )   _1A1 - R04_1-_1A1-R04_4   2012

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    We propose a method for localizing furniture on caster and wheelchair using accelerometers and a floor sensor. The floor sensor measures positions of objects on the floor using laser range finder. However, it is difficult to specify each object since the floor sensor acquire only outline of the object. The accelerometer is attached to the moving furniture and integrated with the floor sensor for localizing and specifying it. In this paper, we extend our previous system for localizing multiple objects and verify effectiveness for a robot working in an everyday environment.

    DOI: 10.1299/jsmermd.2012._1A1-R04_1

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  • レーザレンジファインダによる反射強度と位置計測を用いた床上センシングシステム

    表允晰, 長谷川勉, 曾いん, 辻徳生, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   30th   2012

  • オーダーメイド医療のための軟性肝臓モデルの物性パラメータ推定

    園木秀治, 諸岡健一, 倉爪亮, 橋爪誠, 長谷川勉

    計測自動制御学会九州支部学術講演会予稿集   31st   2012

  • 情報構造化環境における日用品の追跡-移動ロボットによる低レベル視覚記憶の照合と変化検出-

    桑畑舜也, 長谷川勉, 蔡現旭, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   30th   2012

  • A Method for Partitioning Large Dataset into Small Sub-Datasets Using K-means to Construct a Simulator of Stomach Deformation

    TAGUCHI T., MOROOKA K., HASHIZUME M., KURAZUME R., HASEGAWA T.

    Journal of Japan Society of Computer Aided Surgery : J.JSCAS   13 ( 3 )   410 - 411   2011.11

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  • ジストニア・パーキンソン病の定位脳手術支援のための脳座標アトラス

    宮城 靖, 諸岡 健一, 福田 孝一, 倉岡 晃夫, 砂川 賢二, 岡本 剛, 吉浦 敬, 陳 献, 早見 武人, 飛松 省三

    生体医工学   49 ( 5 )   778 - 778   2011.10

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  • Real-time Simulation for Stomach Deformation by Using Multiple Neural Networks

    MOROOKA Ken'ichi, CHEN Xian, HASHIZUME Makoto, HASEGAWA Tsutomu

    IEICE technical report   111 ( 47 )   81 - 86   2011.5

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    This paper presents a method for simulating the behavior of stomach with large-scale deformation. This simulator is generated by the real-time FEM-based analysis by using a neural network. There are various deformation patterns of hollow organs by changing both its shape and volume. In this case, one network can not learn the stomach deformation with a huge number of its deformation pattern. To overcome the problem, we propose a method of constructing the simulator composed of multiple neural networks by 1)partitioning a training dataset into several subsets, and 2)selecting the data included in each subset. From our experimental results, we can conclude that our method can speed up the training process of a neural network while keeping acceptable accuracy.

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  • ジェスチャによる移動ロボットへの動作目標指示と誤差修正

    表允皙, 長谷川勉, 辻徳生, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   29th   2011

  • 加速度センサと床上レーザレンジファインダを用いた移動物体の位置同定

    森達則, 田中真英, 辻徳生, 長谷川勉, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   29th   2011

  • 情報構造化環境における日用品の追跡-視覚付き移動ロボットと固定分散センサ群の連携-

    長谷川勉, 蔡現旭, MOZOS Oscar Martinez, 辻徳生, 諸岡健一, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   29th   2011

  • ドーム型スクリーンを用いた内視鏡外科手術向け立体映像提示システムの開発と手術手技における有用性の評価

    山本 厚行, 星野 洋, 柏木 正徳, 河村 亮, 小岩 弘子, 澤田 一哉, 大内田 研宙, 早見 武人, 諸岡 健一, 剣持 一, 田上 和夫, 橋爪 誠

    VR医学   8 ( 1 )   29 - 40   2010.11

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    内視鏡外科手術に適した立体映像提示システムとして、ドーム型スクリーンを用いた立体映像提示システム(3DD)を開発した。更に本システムが有する奥行き情報の直感的な提示が内視鏡外科手術における鉗子動作に与える影響について、従来の2D CRTモニターと比較し検討した。対象は内視鏡外科手術の経験のない医学生16人と日本内視鏡外科学会認定医の資格を持つ外科医3人とした。縫合結紮手技中の鉗子動作の解析結果より、3DDでは医学生においても他の方向に比べ前後方向に移動距離が大きいという外科医と同様の傾向がみられた。また、鉗子の軸周りの回転を多用していることからも、2Dに比べ鉗子動作の幅を増加することが確認された。

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  • Forceps manipulation for suturing and knot tying through a stereoscopic endoscope

    HAYAMI Takehito, MOROOKA Ken'ichi, YAMAMOTO Atsuyuki, OHUCHIDA Kenoki, HOSHINO Hiroshi, SAWADA Kazuya, UEMURA Munenori, KENMOTSU Hajime, KONISHI Kouzou, IEIRI Satoshi, YOSHIDA Daisuke, MAEDA Takashi, TANOUE Kazuo, HASHIZUME Makoto

    2010 ( 121 )   7 - 10   2010.9

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  • AdaBoostによる気道・食道自動識別--自動気管内挿管システムの開発を目指して

    諸岡 健一, 倉爪 亮, 岩下 友美

    画像ラボ   21 ( 8 )   8 - 13   2010.8

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  • Trachea and Esophagus Classification by AdaBoost

    TAMURA Akito, MOROOKA Ken'ichi, KURAZUME Ryo, IWASHITA Yumi, UCHIDA Seiichi, HARA Kenji, NAKANISHI Yoichi, HASHIZUME Makoto, HASEGAWA Tsutomu

    The IEICE transactions on information and systems   92 ( 12 )   2249 - 2260   2009.12

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    気道確保法の一つである気道挿管では,通常まず喉頭鏡を使って喉頭展開を行い,声門の位置を目視により確認する.しかし実際の医療現場では,上気道閉塞など様々な要因で,声門の位置を目視により確認しづらい場合がある.この不完全な確認が原因で食道へ誤挿管した場合,気道が確保されず危険なだけでなく,無理な目視のために頸椎や歯牙損傷などの合併症を引き起こす危険性がある.安全・確実な気道挿管の実現に向けて,我々は,スタイレット先端に小型カメラを搭載した自動気管内挿管システムを開発することを自指している.本論文では,その要素機能として,カメラから取得される画像から,挿管チューブが気道あるいは食道に挿管されているかを自動的に識別する方法を提案する.本手法は,気道画像には気道周囲の輪状軟骨が特徴的に観察されることから,まずこの環状模様の記述に適した特徴量を定義し,それに基づいた気道・食道識別器をAdaBoostによって構築する.実験の結果,97.6%の高い識別率で気道・食道の判別が可能であり,提案手法の有効性が確認できた.

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  • Efficient Construction of Real-Time Nonliner FEM Simulator for Organ Deformation by Selecting Training Data

    MATSUI H., MROOKA K., UEMURA M., KONISHI K., IEIRI S., HONG J., TOMIKAWA M., TANOUE K., HASIZUME M.

    11 ( 3 )   330 - 331   2009.11

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  • P-2-711 3次元立体映像と2次元映像下における鉗子軌跡の比較 : 利用空間と軸回転量の検討(教育(医学・内視鏡),一般演題(ポスター),第64回日本消化器外科学会総会)

    大内田 研宙, 諸岡 健一, 早見 武人, 山本 厚行, 剣持 一, 小西 晃造, 家入 里志, 田上 和夫, 田中 雅夫, 橋爪 誠

    日本消化器外科学会雑誌   42 ( 7 )   1291 - 1291   2009.7

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  • Innovation of histological techniques in constructing the digitalized human stereotactic brain atlas for the Japanese

    MIYAGI Yasushi, MOROOKA Ken-ichi, FUKUDA Takaichi, OKAMOTO Tsuyoshi, CHEN Xian, HAYAMI Taketo, SUNAGAWA Kenji, TOBIMATSU Shozo, YOSHIURA Takashi, SASAKI Tomio

    48 ( 1 )   68 - 69   2009.6

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  • Human Activity Recognition Based on Camera Selection by Boosting

    SHUTOU Kouji, UCHIDA Seiichi, MOROOKA Ken'ichi, KURAZUME Ryo, HARA Kenji

    IEICE technical report   108 ( 363 )   61 - 66   2008.12

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    A gesture recognition method for multi-camera surveillance is proposed. The proposed method possesses the following three characteristics desirable for practical surveillans. First, the final recognition result is provided by integrating recognition results from individual cameras complementary. Second, camera calibration is not necessary. Third, various sensors other than cameras can be incorporated. The complementary integration is systematically done by an AdaBoost-based training. In addition, we use the local feature which is less discriminative to the important difference among the gestures.

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  • O-2-102 近赤外線スペクトロスコピィNIRSによる内視鏡外科手技中における脳活動パターンの検討(胃 研究4,一般演題(口演),第63回日本消化器外科学会総会)

    大内田 研宙, 山本 厚行, 諸岡 健一, 早見 武人, 剣持 一, 小西 晃造, 家入 里志, 田上 和夫, 田中 雅夫, 橋爪 誠

    日本消化器外科学会雑誌   41 ( 7 )   1221 - 1221   2008.7

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  • 2D/3D Registration by Back Projection and Geometrical Constraints

    KABASHIMA Yuuki, HARA Kenji, KURAZUME Ryo, IWASHITA Yumi, MOROOKA Ken'ichi, UCHIDA Seiichi, HASEGAWA Tsutomu

    The IEICE transactions on information and systems   91 ( 5 )   1380 - 1392   2008.5

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    レンジセンサにより取得した幾何モデルにカラーセンサで撮影したテクスチャ画像を貼り付けて表示するテクスチャマッピングを容易に実現するには,テクスチャ画像と幾何モデルのみからカラー・レンジセンサ間の相対位置関係を知ることが望ましい.本論文では,幾何拘束に基づく大域的手法とエッジの対応付けに基づく局所的手法の組合せにより,センサ間の相対位置・姿勢を初期値の変動にロバストにかつ高精度に推定し,テクスチャ画像と幾何モデルの位置合せを実現する手法を提案する.本手法はまず,テクスチャ画像から稜線と平面領域を抽出する.次に,この稜線と平面領域を幾何モデルに逆投影し,対象における幾何拘束条件を推定しつつ,この拘束条件のもとでセンサ間の相対位置・姿勢の初期推定値を求める.最後に,テクスチャ画像と幾何モデルの各エッジ間の対応付けに基づき,センサ間の相対位置・姿勢を決定する.実験では,エッジ間の対応付けに基づく従来手法と比較して,位置合せの成功率が41%から75%に向上した.

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  • DP-186-4 CyberDomeを用いた新規立体映像提示システムによる奥行き情報提示による内視鏡外科手技の向上 : 鉗子軌跡を含めた検討(第108回日本外科学会定期学術集会)

    大内田 研宙, 山本 厚行, 諸岡 健一, 早見 武人, 剣持 一, 星野 洋, 澤田 一哉, 植村 宗則, 小西 晃造, 家入 里志, 吉田 大輔, 前田 貴司, 田上 和夫, 田中 雅夫, 橋爪 誠

    日本外科学会雑誌   109 ( 2 )   716 - 716   2008.4

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  • 2A1-C04 Classification of trachea and esophagus images for automatic endotracheal intubation

    TAMURA Akito, MOROOKA Ken'ichi, KURAZUME Ryo, IWASHITA Yumi, HASEGAWA Tsutomu, KENMOCHI Hajime, HASHIZUME Makoto, HARADA Taishi, NAKANISHI Yoichi

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2008 ( 0 )   _2A1 - C04_1-_2A1-C04_4   2008

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    We have been developing an automatic endotracheal intubation system using a high performance stylet. This system is composed of an endotracheal stylet and a camera attached at the point of the stylet. One of fundamental functions of the automatic intubation system is to check whether the endotracheal intubation is completed appropriately or not. For achieving this function, this paper presents a method for classifying trachea and esophagus images taken by the mounted camera. The proposed method utilizes circular patterns of cricoid cartilage which is observed in trachea images only. Experimental results show that the proposed method can extract the cricoid patterns in trachea images efficiently and accurately.

    DOI: 10.1299/jsmermd.2008._2A1-C04_1

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  • 逆投影と幾何拘束を用いた2D/3D位置合わせ 3次元環境モデル化のための自動テクスチャマッピング

    椛島佑樹, 原健二, 倉爪亮, 岩下友美, 諸岡健一, 内田誠一, 長谷川勉

    画像ラボ   19 ( 8 )   2008

  • Efficient rotational manipulation of forceps through a stereoscopic endoscope

    HAYAMI T, MOROOKA K, YAMAMOTO A, OHUCHIDA K, HOSHINO H, SAWADA K, UEMURA M, KENMOTSU H, KONISHI K, IEIRI S, YOSHIDA D, MAEDA T, TANOUE K, HASHIZUME M

    Journal of Japan Society of Computer Aided Surgery : J.JSCAS   9 ( 3 )   324 - 325   2007.12

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  • Fast 3D Shape Reconstruction of Moving Object by Parallel Fast Level Set Method

    IWASHITA Yumi, KURAZUME Ryo, HARA Kenji, UCHIDA Seiichi, MOROOKA Ken'ichi, HASEGAWA Tsutomu

    The IEICE transactions on information and systems   90 ( 8 )   1888 - 1899   2007.8

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    多数台のカメラによりシーン内に存在する対象物体の全周の幾何情報及び光学情報を取得し,任意視点からの画像を生成する手法として,視体積交差法と多視点ステレオ法が提案されている.しかしこれらの手法は単一物体あるいはオクルージョンの生じない複数物体を対象とした手法であり,シーン内に複数物体が存在し物体間に相互オクルージョンが生じる場合,それぞれの物体形状を同時に復元することは困難であった.この問題に対し,我々はこれまでに高速な境界追跡手法であるFast Level Set Methodを複数ステレオ距離画像に適用し,複数対象物体の三次元形状をオクルージョンに頑強に復元するシステムを構築している.本論文では,これまでに構築したシステムを8台の計算機からなるPCクラスタへ実装し,Fast Level Set Method処理の並列計算により,より高速な三次元形状の復元を実現する.また対象物体が移動する場合,その移動方向を予測し,移動体を処理する計算機の計算負荷を低減することで,移動体の正確な三次元形状を遅れなく復元する手法を提案する.更に,舞踊の測定実験により,対象が高速に移動しても,従来システムと比較してより正確な三次元形状の復元が可能であることを示す.

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  • D-12-46 Mesh Cross-parameterization based on Competitive Learning and Least-squares Meshes

    Matsui Shun, Morooka Ken'ichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2007 ( 2 )   162 - 162   2007.3

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  • Competitive Learning Based Cross Parameterization between Triangular Meshes

    MATSUI SHUN, MOROOKA KEN'ICHI, NAGAHASHI HIROSHI

    IPSJ SIG Notes   2007 ( 13 )   85 - 90   2007.2

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    Parameterization which maps a triangular mesh to a certain domain is a key technique in mesh modeling and useful for varius CG applications. In this paper, we propose a new method for mapping one mesh to another. Our method is based on a learning algorithm of Self-organizing Map and Least-squares Mesh. These techniques enable us to make a parameterization taking account of structural features of meshes.

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  • 自己組織化可変モデル:目標曲面への三次元物体メッシュモデルの写像

    諸岡健一, 松井瞬, 長橋宏

    電子情報通信学会論文誌D   90-D ( 3 )   908 - 917   2007

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  • Face Detection by Generating and Selecting Features Based on Kullback-Leibler Divergence

    Ken'ichi Morooka, Junya Arakawa, Hiroshi Nagahashi

    Electronics and Communications in Japan   90 ( 10 )   29--39   2007

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  • Cross-parameterization for triangular meshes with semantic features Reviewed

    Shun Matsui, Kota Aoki, Hiroshi Nagahashi, Ken'ichi Morooka

    PACIFIC GRAPHICS 2007: 15TH PACIFIC CONFERENCE ON COMPUTER GRAPHICS AND APPLICATIONS   457 - +   2007

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    In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings(DGP) such as morphing, shape blending, texture transfer re-meshing and so on. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of your method is to combine a competitive learning and a least-square mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses. We show the effectiveness of our approach by giving some examples of its applications.

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  • 自律的な判断能力を備えるキャラクタを利用する動作アニメーション作成システム

    熊谷 隆, 長橋 宏, 諸岡 健一

    研究会講演予稿   224   37 - 44   2006.3

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  • 仮想環境における物体形状モデルの動作獲得

    井上 憲, 諸岡 健一, 長橋 宏

    研究会講演予稿   224   65 - 72   2006.3

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  • 3-D Morphing by Direct Mapping between Mesh Models Using Self-organizing Deformable Model

    MATSUI SHUN, MOROOKA KEN'ICHI, NAGAHASHI HIROSHI

    IEICE technical report   105 ( 673 )   77 - 84   2006.3

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    This paper presents a new method for 3-D morphing between mesh models using a Self-organizing Deformable Model (SDM). The SDM is a mesh model which deforms by learning vertex coordinates on target surface. Since the SDM doesn't limit target shapes, we can use various mesh models as SDMs. Therefore, we use SDM for determining correspondence between models. Also, SDM enables user to define the correspondence by model features. We demonstrated some examples for morphing.

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  • 3-D Morphing by Direct Mapping between Mesh Models Using Self-organizing Deformable Model

    MATSUI SHUN, MOROOKA KEN'ICHI, NAGAHASHI HIROSHI

    IPSJ SIG Notes. CVIM   2006 ( 25 )   77 - 84   2006.3

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    This paper presents a new method for 3-D morphing between mesh models using a Self-organizing Deformable Model(SDM). The SDM is a mesh model which deforms by learning vertex coordinates on target surface. Since the SDM doesn't limit target shapes, we can use various mesh models as SDMs. Therefore, we use SDM for determining correspondence between models. Also, SDM enables user to define the correspondence by model features. We demonstrated some examples for morphing.

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    Other Link: http://id.nii.ac.jp/1001/00052219/

  • D-11-115 Distance measurement of a real world environment by using an active camera system

    Won Jonghoon, Morooka Ken'ichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2006 ( 2 )   115 - 115   2006.3

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  • D-12-7 Motion generation of 3D object model by using Genetic Algorithm

    Inoue Ken, Morooka Ken'ichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2006 ( 2 )   139 - 139   2006.3

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  • D-12-2 Interactive Motion Synthesis by Using Autonomous Character

    Kumagai Takashi, Morooka Ken'ichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2006 ( 2 )   134 - 134   2006.3

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  • DISTANCE MEASUREMENT OF A REAL-WORLD ENVIRONMENT USING AN ACTIVE CAMERA SYSTEM

    WON Jonghoon, MOROOKA Kenichi, NAGAHASHI Hiroshi

    IEICE technical report   105 ( 500 )   177 - 182   2006.1

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    In this paper, we address an active way of 3-D distance measurement. Generally, the real-world environment is composed of various kinds of objects. The most adequate parameters of a measurement system may be different in respective objects. It needs to adjust the system parameters such as camera position, pan and tilt angles, focal length, zoom and so on. First, we perform 3-dimensional measurement of the whole scene by a parallel stereo vision and choose some regions for targets and estimate their positions from the measurement result of the whole scene. After adjusting viewing direction and resolution to a target region, we perform a fine measurement again.

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  • AdaBoost-based classification for multicamera system

    Shutou Kouji, Morooka Kenichi, Uchida Seiichi, Kurazume Ryo, Hara Kenji

    Record of Joint Conference of Electrical and Electronics Engineers in Kyushu   2006 ( 0 )   89 - 89   2006

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    DOI: 10.11527/jceeek.2006.0.89.0

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  • Integration of Range Images with Different Resolusions for 3D Object Modeling

    MOROOKA Ken'ichi, KANG Yousun, NAGAHASHI Hiroshi

    The Journal of the Institute of Television Engineers of Japan   60 ( 3 )   409 - 417   2006

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    This paper proposes a new method for integrating range images with different resolutions to generate an entire 3D model. The modeling method is based on the fact range images need an overlapping area between the images for the registration process. Thus, we integrate data from two range images into one surface model, and represent the overlapping area with triangular patches. Few conventional methods have paid attention to integrating images with different resolutions. Moreover, most conventional methods find corresponding points by using the distance between points. However, it sometimes causes wrong corresponding points in convex and concave surfaces. We propose a new integration method that uses geometric and topological information. Experimental results showed that this method is efficient for integrating multiple range images.

    DOI: 10.3169/itej.60.409

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  • 解像度の異なるデータでの距離画像統合

    長橋宏, 諸岡健一

    日本工業出版株式会社「画像ラボ」   17 ( 11 )   15 - 18   2006

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  • Kullback-Leibler情報量に基づく特徴の生成と選択による顔検出

    諸岡健一, 荒川純也, 長橋宏

    電子情報通信学会論文誌   J89-D-II ( 3 )   530 - 540   2006

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  • A Method for Projecting Mesh Model of 3D Object Onto Arbitrary Surface

    MOROOKA KENICHI, NAGAHASHI HIROSHI

    IPSJ SIG Notes. CVIM   2005 ( 88 )   99 - 106   2005.9

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    This paper presents a new method for projecting a mesh model of a source object onto a surface of an arbitrary target object. A deformable model, called Self-organizing Deformable Model(SDM), is deformed so that the shape of the model is fitted to points on the target object. Then, we introduce the idea of combining a competitive learning and an energy minimization into the SDM deformation. Our method is a powerful tool in the areas of computer vision and computer graphics. For example, it enables to map mesh models onto various kinds of target surfaces compared with traditional methods for a surface parameterization, which have only focused on specified target surface. Also the SDM can reconstruct shapes of target objects similar with general deformable models.

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    Other Link: http://id.nii.ac.jp/1001/00052328/

  • D-12-87 Face Reconstruction from Stereo Images Based on 3D Face Model

    Shitomi Takuya, Morooka Kenichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2005 ( 2 )   237 - 237   2005.3

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  • D-12-76 3D Object Morphing Based on a Map Method that Uses Competitive Learnig

    Takagi Hiroyuki, Morooka Kenichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2005 ( 2 )   226 - 226   2005.3

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  • D-7-3 THE STUDY ABOUT MOVEMENT GENERATION OF THREE DEMENSIONS OF GEOMETRIC MODELS ON THE BASIS OF RECEPTOR-PRODUCT COUPLING MODEL

    Kondou Shousuke, morooka Kenniti, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2005 ( 1 )   60 - 60   2005.3

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  • D-12-92 Face Recognition Using Probablistic Learning Models

    Kasai Wataru, Morooka Kenichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2005 ( 2 )   242 - 242   2005.3

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  • D-12-115 Motion Generation of 3D object Model by deforming its shape based on Reiforcement Learning

    Osugi Koji, Morooka Kenichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2005 ( 2 )   265 - 265   2005.3

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  • 強化学習と隠れマルコフモデルの結合による自律的な動作認識

    諸岡健一, 浜元和久, 長橋宏

    電子情報通信学会論文誌D-II   J88-D-II ( 7 )   1269 - 1277   2005

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  • D-11-90 3D Object Modeling from Range Images with Different Resolutions

    Ikesako Kumiko, Morooka Kennichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   90 - 90   2005

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  • View Space Representation and Viewpoint Planning Computation for Automatic 3D Object Modeling

    Ken'ichi Morooka, Hongbin Zha, Tsutomu Hasegawa

    Systems and Computers in Japan   35 ( 4 )   60 - 71   2004.4

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    Viewpoint planning plays an important role in automatic 3D model generation. We have proposed an optimal viewpoint planning [9] for the purpose of model generation by merging images acquired from multiple viewpoints on a sphere that encircles object. This method uses a discretized approximate sphere which involves problems of sphere tessellation and of sphere representation. One must consider that the scanned surface of an object is projected onto a sphere, and that the computational cost increases directly with the number of images. However, there are no appropriate sphere representation methods available. This paper proposes a new representation method based on a 2D array, and develops a new viewpoint planning algorithm. The proposed system is verified by algorithmic analysis and experiments using a real modeling system. © 2004 Wiley Periodicals, Inc.

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  • View Space Representation and Viewpoint Planning Computation for Automatic 3D Object Modeling

    Ken'ichi Morooka, Hongbin Zha, Tsutomu Hasegawa

    Systems and Computers in Japan   35 ( 4 )   60 - 71   2004.4

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    Viewpoint planning plays an important role in automatic 3D model generation. We have proposed an optimal viewpoint planning [9] for the purpose of model generation by merging images acquired from multiple viewpoints on a sphere that encircles object. This method uses a discretized approximate sphere which involves problems of sphere tessellation and of sphere representation. One must consider that the scanned surface of an object is projected onto a sphere, and that the computational cost increases directly with the number of images. However, there are no appropriate sphere representation methods available. This paper proposes a new representation method based on a 2D array, and develops a new viewpoint planning algorithm. The proposed system is verified by algorithmic analysis and experiments using a real modeling system. © 2004 Wiley Periodicals, Inc.

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  • A Face Detection Method based on Selection and Generation of High Dimensional Features

    ARAKAWA Junya, MOROOKA Ken'ichi, NAGAHASHI Hiroshi

    Technical report of IEICE. PRMU   103 ( 737 )   115 - 120   2004.3

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    In order to recognize human face by machine, it is necessary to firstly detect human face regions from images. It is not an easy problem because human face is nonrigid. We propose a novel face detection algorithm by selecting useful features from high dimensional features and generating new ones. Our algorithm is composed of the following steps; 1. Many kernel features are generated based on Kullback-Leiblber Divergence: 2. A boosting algorithm selects some useful features for face detection: 3. Steps 1 and 2 are performed iteratively. Our algorithm achieves almost equal or better detection rate than that of a Support Vector Machine (SVM). It also achieves nearly one-tenth calculation cost of the SVM.

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  • Multi-class Pattern Recognition based on Compression of High Dimensional Features

    FUJIKAWA Yusuke, MOROOKA Ken'ichi, NAGAHASHI Hiroshi

    Technical report of IEICE. PRMU   103 ( 737 )   97 - 102   2004.3

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    This paper proposes a framework for multi-class pattern recognition based on compression of high dimensional features. In pattern recognition, since it is generally difficult to specify features that are suitable for classification of each class in advance, it is important to take the features into consideration broadly from various viewpoints in feature extraction from input pattern. However, if high dimensional features acquired as a result are directly used for classification, various problems such as the explosion of amount of calculation will be caused. Then, high dimensional features in which many features considered to be useful in classification were included are effectively compressed by a neural network to low dimensional features. This realizes highly precise and high-speed recognition.

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  • Texture Classification using Hierarchical Discriminant Analysis

    YASUOKA Syuichi, MOROOKA Kenichi, NAGAHASHI Hiroshi

    Technical report of IEICE. PRMU   103 ( 737 )   91 - 96   2004.3

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    In this paper, we propose a new multi-class discriminating method by using hierarchical discriminant spaces. The Fisher's method is used for a multi-class classification problem. However, due to overlapping of some classes in a discriminant space, the precision of their discrimination becomes worse. In order to take such overlap of classes into consideration, our approach builds a new space for overlapped classes. We obtain hierarchical discriminant space by recursively grouping the overlapped classes. In the experiment, texture images of many classes are examined based on the proposed method, and we show the outstanding result compared with the conventional method.

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  • D-8-26 Automatic Learning for Recognition and Generation of Motions

    Hamamoto Kazuhisa, Morooka Kenichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2004 ( 1 )   113 - 113   2004.3

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  • D-12-64 Face Detection Using Generation and Selection of Kernel Features

    Arakawa Junya, Morooka Ken'ichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2004 ( 2 )   230 - 230   2004.3

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  • D-12-75 Multi-class Pattern Recognition by using Compression of High Dimensional Features

    FUJIKAWA Yusuke, MOROOKA Ken'ichi, NAGAHASHI Hiroshi

    Proceedings of the IEICE General Conference   2004 ( 2 )   241 - 241   2004.3

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  • D-12-76 Hierarchical Multi-Class Discriminant method For Texture Classification

    Yasuoka Syuichi, Morooka Kenichi, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2004 ( 2 )   242 - 242   2004.3

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  • Acquisition of three - dimensional shape with an active camera system

    WON Jonghoion, MOROOKA Kenichi, NAGAHASHI Hiroshi

    IPSJ SIG Notes. CVIM   2004 ( 6 )   23 - 30   2004.1

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    There have been a number of studies, which were performed for 3-dimensional measurement by means of fixed camera parameters. In this paper, we will perform 3-dimensional distance measurement in the whole scene, and then measure distance of each object in the scene. It is our aim to establish the position relation between a scene and each object, and the position relation among the objects. When we perform 3-dimensional measurement, we will measure an object by adjusting focus length and resolution to the property of each object. Our methodology makes it possible to perform 3-dimensional measurement actively and to shorten the measurement time, and to reduce the amount of data.

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  • A method for detecting human face region based on generation and selection of kernel features

    J Arakawa, K Morooka, Y Kang, H Nagahashi

    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7   2191 - 2196   2004

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    Recent researches for detecting face regions from images have paid attention to high dimensional kernel features(KFs), which are obtained by a non-linear transformation of original features extracted from images. A Support Vector Machine(SVM) is one of the most prominent learning algorithms for KFs. However; SVM is time-consuming because of needing a large number of KFs to improve the accuracy of the classification. This paper proposes a new method that constructs a classifier between face and non-face regions by generating and choosing KFs based on Kullback-Leibler Divergence(KLD). The KLD means a distance between two distributions of face and non-face data under a given KF and some KFs of large KLDs are selected for the face detection. Moreover; the use of KLD enables us to generate new KFs' and to deal with different kinds of KFs concurrently Some experiments show that our method can reduce the number of KFs much more than SVM, and achieve almost equal or better detection rate than that of SWM.

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  • Motion recognition by combining HMM and reinforcement learning

    K Hamamoto, K Morooka, H Nagahashi

    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7   5259 - 5264   2004

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    It is difficult to give a robot all possible motions beforehand in a certain environment. Therefore, the robot needs to learn how to recognize others' motions and to generate its own motions autonomously for working well. These learning algorithms need an efficient way to make recognition and generation of motions work together, because the), take many computing resources. This paper focuses on a generation-based recognition.
    Our system consists of recognition and generation modules. The former and latter are constructed from left-to-right Hidden Markov Models (HMM) and Reinforcement Learning (RL), respectively. When a HMM in recognition module does not work enough, the model parameters of HMM are re-estimated by using a state-value function of RL in generation module. The proposed method enables us to improve the reliability of the HMM.

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  • A method for detecting human face region based on generation and selection of kernel features

    J Arakawa, K Morooka, Y Kang, H Nagahashi

    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7   2191 - 2196   2004

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    Recent researches for detecting face regions from images have paid attention to high dimensional kernel features(KFs), which are obtained by a non-linear transformation of original features extracted from images. A Support Vector Machine(SVM) is one of the most prominent learning algorithms for KFs. However; SVM is time-consuming because of needing a large number of KFs to improve the accuracy of the classification. This paper proposes a new method that constructs a classifier between face and non-face regions by generating and choosing KFs based on Kullback-Leibler Divergence(KLD). The KLD means a distance between two distributions of face and non-face data under a given KF and some KFs of large KLDs are selected for the face detection. Moreover; the use of KLD enables us to generate new KFs' and to deal with different kinds of KFs concurrently Some experiments show that our method can reduce the number of KFs much more than SVM, and achieve almost equal or better detection rate than that of SWM.

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  • 3-D morphing between objects with different topologies using deformable models

    Ken'ichi Morooka, Hiroshi Nagahashi

    Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers   58 ( 5 )   713 - 720   2004

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    3-D morphing is shape transformation from one object to another. This paper presents a new method of morphing objects with different topologies. The basic idea behind our method is to generate an approximate model of a given object by using a deformable model, called the Active Balloon Model(ABM). Since the data structure for each object is similar to that of the original ASM, it is easy to find correspondence between two models by utilizing ABMs. We also propose a new method of generating intermediate models during morphing. This method uses various kinds of time functions according to the types of topological changes. These time functions enable us to control topological changes arbitrarily.

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  • Motion recognition by combining HMM and reinforcement learning

    K Hamamoto, K Morooka, H Nagahashi

    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7   5259 - 5264   2004

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    It is difficult to give a robot all possible motions beforehand in a certain environment. Therefore, the robot needs to learn how to recognize others' motions and to generate its own motions autonomously for working well. These learning algorithms need an efficient way to make recognition and generation of motions work together, because the), take many computing resources. This paper focuses on a generation-based recognition.
    Our system consists of recognition and generation modules. The former and latter are constructed from left-to-right Hidden Markov Models (HMM) and Reinforcement Learning (RL), respectively. When a HMM in recognition module does not work enough, the model parameters of HMM are re-estimated by using a state-value function of RL in generation module. The proposed method enables us to improve the reliability of the HMM.

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  • 3D Polygon Model Generation Based on Geometrical Structure Extracted from an Object

    Takagi Hiroyuki, Morooka Kenichi, Hasegawa Osamu, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2003 ( 2 )   259 - 259   2003.3

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  • Interactive Acquisition of Concepts from Dynamic Patterns

    MATSUMOTO Koichiro, MOROOKA Kenichi, HASEGAWA Osamu, NAGAHASHI Hiroshi

    Proceedings of the IEICE General Conference   2003 ( 2 )   176 - 176   2003.3

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  • Synthesis of Video-based Animation

    Aoki Kohta, Morooka Kenichi, Hasegawa Osamu, Nagahashi Hiroshi

    Proceedings of the IEICE General Conference   2003 ( 2 )   252 - 252   2003.3

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  • 自然現象の計測と学習に基づく映像生成 Reviewed

    青木 工太, 諸岡 健一, 長谷川 修, 長橋 宏

    第9回画像センシングシンポジウム講演論文集   259 - 264   2003

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  • Active balloon model based on 3D skeleton extraction by competitive learning

    K Morooka, H Takagi, H Nagahashi

    FOURTH INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS   87 - 94   2003

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    This paper focuses on the polygonal representation of a 3D object model which is composed of a lot of points on the object surface. In the 3D animation, it is sometimes necessary to use multi-resolution representation of a model to cope with various situations, and it is preferable to represent the models with different resolutions in a certain unified data structure. In order to meet theses requirements, this paper presents a new method which generates the approximated model of an original one by using multiple deformable models, called active balloon models (ABMs). We extract the three-dimensional skeleton of the object. The obtained skeleton comprises nodes and edges, and represents the structure of the object. Based on the skeleton, the approximated model is generated by deforming the ABMs. Some experimental works are made to verify the capability of our method.

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  • Active balloon model based on 3D skeleton extraction by competitive learning

    K Morooka, H Takagi, H Nagahashi

    FOURTH INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS   87 - 94   2003

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    This paper focuses on the polygonal representation of a 3D object model which is composed of a lot of points on the object surface. In the 3D animation, it is sometimes necessary to use multi-resolution representation of a model to cope with various situations, and it is preferable to represent the models with different resolutions in a certain unified data structure. In order to meet theses requirements, this paper presents a new method which generates the approximated model of an original one by using multiple deformable models, called active balloon models (ABMs). We extract the three-dimensional skeleton of the object. The obtained skeleton comprises nodes and edges, and represents the structure of the object. Based on the skeleton, the approximated model is generated by deforming the ABMs. Some experimental works are made to verify the capability of our method.

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  • 実写映像に基づくアニメーションの生成

    青木 工太, 諸岡 健一, 長谷川 修, 長橋 宏

    2003年電子情報通信学会総合大会講演論文集   102   31 - 36   2003

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  • Animation synthesis by observation and learning Reviewed

    K Aoki, K Morooka, O Hasegawa, H Nagahashi

    2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS   3   1258 - 1263   2003

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    Language:English   Publisher:IEEE  

    This paper proposes a method for the animation of things in nature based on the observation of natural phenomena and on the synthesis of their behavioral patterns using machine learning methods. The natural phenomenon to be animated is recorded using a video camera, and its characteristic behavior is captured. A data sequence representing the subject behavior is obtained from the captured video. By learning the inherent structure in the feature space of some sample data, the learned model can synthesize a novel data sequence from the existing sequences. The generated sequences of behavioral patterns could differ from every original data sequence but preserve characteristics of the subject behavior. We demonstrate the natural animation synthesis through such behavioral pattern sequences, and produce some realistic animation which depict the subject.

    DOI: 10.1109/CIRA.2003.1222177

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  • State Generalization in Reinforcement Learning without Observing States after Transition

    HAMAMOTO Kazuhisa, MOROOKA Ken'ich, NAGAHASHI Hiroshi

    Proceedings of the IEICE General Conference   2002 ( 1 )   110 - 110   2002.3

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  • Shape Transformation between Objects with Different Topologies Reviewed

    Ken'ichi Morooka, Kohta Sugisawa, Hiroshi Nagahashi

    NICOGRAPH International 2002   85 - 90   2002

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  • 3D Morphing between Objects with Different Topologies

    IASTED International Conference on Computer Graphics and Imaging   2002

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  • 3D Morphing between Objects with Different Topologies

    IASTED International Conference on Computer Graphics and Imaging   2002

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  • Shape Transformation between Objects with Different Topologies

    MOROOKA K.

    Proc. NICOGRAPH International 2002   85 - 90   2002

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  • 幾何情報と位相情報を用いた3次元物体形状の復元

    諸岡健一, 高鳥暁彦, 長橋宏

    画像の認識・理解シンポジウム   2002

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  • A Method for Embedding Watermarks in Bezier Parametric Curve Models

    MITSUHASHI Rikima, MOROOKA Ken'ichi, NAGAHASHI Hiroshi

    Proceedings of the IEICE General Conference   2001 ( 2 )   104 - 104   2001.3

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  • Extraction of Arrangement Pattern in Musical Composition Using Neural Network

    NAGATA Youichi, MOROOKA Ken'ichi, NAGAHASHI Hiroshi

    Proceedings of the IEICE General Conference   2001 ( 2 )   324 - 324   2001.3

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  • 3次元物体自動モデリングのための視点空間表現と視点計画計算法

    電子情報通信学会論文誌 (D-II)   J84-D-II ( 1 )   64 - 74   2001

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  • 3D Shape Recovery From Range Image Using Active Balloon Model

    Ken'cihi Morooka, Hiroshi Nagahashi

    Proceedings of International Workshop on Advanced Image Technology 2001 (IWAIT'2001)   31 - 36   2001

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  • View Space Representation and Viewpoint Planning Computation for Automatic 3-D Object Modeling

    MOROOKA Ken'ichi, ZHA Hongbin, HASEGAWA Tsutomu

    Transactions of the Institute of Electronics, Information and Communication Engineers D-II   J84-D-II ( 1 )   64 - 74   2001

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  • 位相が異なる3次元物体モデル間のモーフィングに関する研究

    3次元画像コンファレンス2001   85 - 88   2001

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  • アクティブバルーンモデルを用いた距離画像からの3次元物体表面再構成

    電子情報通信学会講演論文集   情報システム2   116   2001

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  • Next Best Viewpoint(NBV) Planning for Generating 3-D Object Models from Sequence of Range Images

    Transactions of the Institute of Electronics, Information and Communication Engineers D-II   J82-D-II ( 3 )   371 - 381   1999

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  • Computations on a Spherical View Space for Efficient Planning of Viewpoints in 3-D Object Modeling

    Ken'cihi Morooka, Hongbin Zha, Tsutomu Hasegawa

    Proceedings of International Conference on 3-D Digital Imaging and Modeling   138 - 147   1999

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  • 複数の距離画像の統合による3次元物体モデル生成のための視点計画

    電子情報通信学会論文誌 (D-II)   J82-D-II ( 3 )   371 - 381   1999

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  • Next Best Viewpoint(NBV) Planning for Active Object Modeling Based on a Learning-by-showing Approach

    Ken'ichi Morooka, Hongbin Zha, Tsutomu Hasegawa

    Proceedings of International Conference on Pattern Recognition   677 - 681   1998

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    Authorship:Lead author   Language:English   Publisher:IEEE COMPUTER SOC  

    The paper presents a method of creating a complete model of a curved object from a sequence of range images acquired by a fixed range finder. To accomplish the modeling fast and accurately in an optimal manner, we propose a new on-line viewpoint planning algorithm to choose the Next Best Viewpoint (NBV) based on the already obtained partial model. The NBV is determined by evaluating factors such as possibility of merging new data, local shape changes, registration accuracy and control point distribution.

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  • Next Best Viewpoint(NBV) Planning for Active Object Modeling Based on a Learning-by-showing Approach

    Ken'ichi Morooka, Hongbin Zha, Tsutomu Hasegawa

    Proceedings of Asian Conference on Computer Vision   185 - 192   1998

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  • Active modeling of 3-D objects: Planning on the next best pose (NBP) for acquiring range images

    HB Zha, K Morooka, T Hasegawa, T Nagata

    INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS   68 - 75   1997

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    Language:English   Publisher:I E E E, COMPUTER SOC PRESS  

    We propose a new method of creating a complete model of a curved object from multiple range images acquired by showing it at different poses. The pose of the object is changed by a manipulator in order to view the object from some specified viewpoints. The pose is planned after each new image as merged into a unified representation. A rating function for the planning is defined to tate into consideration the factors such as possibility of merging new data, registration. accuracy and control point selection.

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  • Active modeling of 3-D objects: Planning on the next best pose (NBP) for acquiring range images

    HB Zha, K Morooka, T Hasegawa, T Nagata

    INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS   68 - 75   1997

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    Language:English   Publisher:I E E E, COMPUTER SOC PRESS  

    We propose a new method of creating a complete model of a curved object from multiple range images acquired by showing it at different poses. The pose of the object is changed by a manipulator in order to view the object from some specified viewpoints. The pose is planned after each new image as merged into a unified representation. A rating function for the planning is defined to tate into consideration the factors such as possibility of merging new data, registration. accuracy and control point selection.

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Presentations

  • 深層学習によるNBI内視鏡画像を用いた上部消化管腫瘍抽出

    李澤昊, 諸岡健一, 江端由穂, 蓮田博文, 宮内翔子, 太田光彦

    電子情報通信学会医用画像研究会 MI2022-54  2022.9.15 

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    Event date: 2022.9.15

    Language:Japanese   Presentation type:Oral presentation (general)  

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  • 患者の心臓動的形状とメタデータを用いた虚血性心疾患診断システムの構築

    宮内翔子, 諸岡健一, 倉爪亮

    電子情報通信学会医用画像研究会  2022.7.8 

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    Event date: 2022.7.8 - 2022.7.9

    Language:Japanese  

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  • コンピューターシミュレーションの医療への応用 Invited

    諸岡健一

    第61回日本生体医工学会大会  2022.6.30 

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    Event date: 2022.6.28 - 2022.6.30

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

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  • Multiple Instance Learningによる大腸病理画像からの癌再発予測システムの構築

    大森一輝, 諸岡健一, 中西良太, 宮内翔子, 沖英次, 吉住朋晴

    電子情報通信学会医用画像研究会 MI2022-17  2022.5.19 

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    Event date: 2022.5.19 - 2022.5.20

    Language:Japanese  

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  • 多重焦点画像列を用いたOptical Projection Tomographyの DNN用ライブラリ実装

    石井 直行, 長原 一, 諸岡 健一

    第229回CVIM研究発表会  2022.3.10 

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    Event date: 2022.3.10 - 2022.3.11

    Language:Japanese   Presentation type:Oral presentation (general)  

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  • 3次元AIを用いた画像情報処理による医療支援 Invited

    諸岡健一

    第429回CBI学会講演会  2022.1.13 

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    Event date: 2022.1.13

    Language:Japanese  

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  • AI × 画像の活用 ~ AIベース医療支援システムの紹介と事業での活用~ Invited

    諸岡健一

    システムエンジニアリング岡山 AI勉強会  2021.11.16 

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    Event date: 2021.11.16

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  • 画像情報処理による医療AI Invited

    諸岡健一

    岡山大学Society5.0シンポジウム「AI×医療×工学~AI医療応用最前線~」  2021.9.7 

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    Event date: 2021.9.7

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  • 骨格形状への機械学習の応用 Invited

    諸岡健一

    第41回日本骨形態計測学会  2021.7.2 

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    Event date: 2021.7.1 - 2021.7.3

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

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  • 3次元形状情報と画像情報処理によるAIベース診断・治療支援システム Invited

    諸岡健一

    第43回岡山歯学会学術集会  2022.12.11 

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  • ⽴体的画像認識AIによる細胞診断⽀援システム Invited

    諸岡健一

    第2回「脳波判読と診断支援のDX」ワークショップ  2022.12.10 

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  • 3次元心臓モデルための動作補間手法の構築

    チャン チュジェ, 宮内 翔子, 諸岡 健一, 倉爪 亮

    情報処理学会コンピュータビジョンとイメージメディア研究会  2022.11.19 

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  • 陰関数表現を用いた同一構造を持つ3次元物体メッシュモデル生成法の構築

    板谷 響, 宮内 翔子, 諸岡 健一

    情報処理学会コンピュータビジョンとイメージメディア研究会  2022.11.19 

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  • 画像認識による未固定大腸癌切除標本中の病変部検出と腫瘍正常識別および深達度予測

    寅田信博, 大内田研宙, 諸岡健一, 永井俊太郎, 水内祐介, 河田純, 小田義直, 中村雅史

    JDDW  2021.11 

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  • Ladder Variational Autoencoderを用いた動的心臓形状の特徴量抽出

    田 偉業, 宮内 翔子, 諸岡 健一, 倉爪 亮

    第40回計測自動制御学会九州支部学術講演会  2021.11 

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  • 同一構造を持つ3次元物体メッシュモデルの識別に適したGraph Convolutional Network構造の検討

    板谷響, 宮内翔子, 諸岡健一

    画像の認識・理解シンポジウムMIRU2021  2021.7 

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  • 触媒ナノ粒子の画像解析における転移学習の有効性の評価

    小山 朗, 宮内 翔子, 諸岡 健一, 北條 元, 永長 久寛, 村上 恭和

    日本顕微鏡学会 第77回学術講演会  2021.6 

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  • Graph Convolutional Networkによる口唇口蓋裂患者の咬合評価

    宮内翔子, 渡邉匠吾, 板谷 響, 谷川千尋, 谷村百和子, 山城 隆, 長原 一, 諸岡健一

    電子情報通信学会医用画像研究会 MI2020-51  2021.3 

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  • 転移学習を用いた触媒ナノ粒子の電子顕微鏡画像の解析

    小山 朗, 宮内 翔子, 太田 潤, 諸岡 健一, 高橋 由夫, 谷垣 俊明, 品田 博之, 北條 元, 永長 久寛, 村上 恭和

    日本金属学会 第167回講演大会  2020.9 

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  • 転移学習を用いた畳み込みニューラルネットワークによる触媒ナノ粒子の画像解析

    小山 朗, 太田 潤, 宮内 翔子, 諸岡 健一, 北條 元, 永長 久寛, 中島 宏, 村上 恭和

    日本顕微鏡学会 第76回学術講演会  2020.5 

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  • 日用品形状と人動作の統計的学習に基づくアーム動作生成

    田島翔, 川久保淳志, 辻徳生, 鈴木陽介, 渡辺哲陽, 宮内翔子, 諸岡健一, 原田研介, 関啓明

    ロボティクス・メカトロニクス講演会講演概要集  2020.5 

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  • 知的画像情報処理による診断・治療支援システム Invited

    諸岡健一

    第59回日本生体医工学会大会大会  2020.5 

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  • 1体の日本人脳を用いた脳の解剖学的地図「3次元脳図譜」の構築

    野田陽太, 諸岡健一, 宮城靖, 福田孝一, 宮内翔子, 倉爪亮

    電子情報通信学会技術研究報告 MI2019-65  2020.1 

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Awards

  • Uchida Best Paper Award

    2017.6   医用画像情報学会   A framework for estimating four-dimensional dose distributions during stereotactic body radiation therapy based on a 2D/3D registration technique with an adaptive transformation parameter approach

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  • Best poster award

    2017.1   International Forum on Medical Imaging in Asia 2017   Simulation of Deforming Human Tissue by Multiple Deep Neural Networks

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  • Best poster award

    2015.12   The Eleventh Joint Workshop on Machine Perception and Robotics   Identification of Cone Cell Distribution Pattern in Retina Image

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  • Presentation award

    2015.1   第15回計測自動制御学会・SI2014   能動的物体認識のための視点計画法

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  • Presentation award

    2013.1   第13回計測自動制御学会・SI2012   レーザレンジファインダの反射強度を利用した物体及びロボットの位置計測

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  • Presentation award

    2010.6   第49回日本定位・機能神経外科学会  

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  • Goode design award

    2007.12   (財)日本産業デザイン振興会  

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  • Uchida Best Paper Award

    2006.5   映像情報メディア学会   可変モデルを用いた異なる位相を持つ3次元物体モデルのモーフィング

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Research Projects

  • 多元歯形状データベースに基づくAIベース歯科治療支援システムの開発

    Grant number:22H03615  2022.04 - 2025.03

    日本学術振興会  科学研究費助成事業 基盤研究(B)  基盤研究(B)

    諸岡健一, 上岡寛, 宮内翔子, 河野加奈

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    Grant amount:\17550000 ( Direct expense: \13500000 、 Indirect expense:\4050000 )

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  • 3D画像認識AIによる革新的癌診断支援システムの構築

    2017.10 - 2023.03

    科学技術振興機構  CREST 

    諸岡健一

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  • Japan-U.S. collaboration to find novel biomarkers of CDK4/6 inhibitor using explainable deep learning and spatial genetic analysis.

    Grant number:22KK0118  2022.10 - 2026.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Fund for the Promotion of Joint International Research (Fostering Joint International Research (B))  Fund for the Promotion of Joint International Research (Fostering Joint International Research (B))

    谷岡 真樹, 遠西 大輔, 枝園 忠彦, 宮内 翔子, 諸岡 健一, 柳井 広之

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    Grant amount:\20150000 ( Direct expense: \15500000 、 Indirect expense:\4650000 )

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  • Development of a novel artificial intelligence related predictive algorism to detect a fatal electrocardiographic changes leading to a critical accident during marathon

    Grant number:21K08107  2021.04 - 2024.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    笠原 真悟, 森田 瑞樹, 藤井 泰宏, 平井 健太, 逢坂 大樹, 諸岡 健一, 坂野 紀子

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    Grant amount:\4160000 ( Direct expense: \3200000 、 Indirect expense:\960000 )

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  • AIに基づく外科医の術中技能の定量化による次世代低侵襲手術手技訓練システム

    Grant number:19H04139  2019.04 - 2022.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

    諸岡 健一, 大内田 研宙, 倉爪 亮, 河村 晃宏, 宮内 翔子

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    Grant amount:\17160000 ( Direct expense: \13200000 、 Indirect expense:\3960000 )

    当該研究は,腹腔鏡下手術の安全性・確実性の向上を目指し, 申請者が有する実時間有限要素解析システム(neuroFEM) と,術中内視鏡画像を 融合することで,手術中の外科医の手術手技と,それに伴う人体組織の振舞いを計測し,それに基づいた次世代の低侵襲手術手技訓練システム の開発を目的とする.
    令和元年度では,neuroFEM を拡張し,異なる物性の組織間の相互作用を考慮した実時間有限要素解析システムmultiphysics DeepFEM (mDeepFEM) の構築と ,それによる患者臓器形状の計測法の開発について研究を行った.まず,neuroFEMの入力は位置と周囲からの応力を入力としていたため,それに物体の硬さを表す物性値を入力パラメータを加えたシステムに拡張した.ここで,これまで開発したneuroFEMはC言語で作成していたが,その学習に要する時間が膨大となることが問題であった.そこで,深層学習で用いられるプログラミング言語PyTorchに書き換えることで,学習の効率化を図った.その結果,これまでのneuroFEMの精度を保ちつつ,学習時間をほぼ半分程度に短縮することが可能となった.
    また,次年度では内視鏡画像から術具領域を自動的に抽出するシステムについて研究を行う予定である.この研究では,大量の内視鏡画像が必要であり,そのため内視鏡画像を収集し,各画像から術具領域を手動で抽出・ラベル付けをすることで,システム開発のためのデータベース構築に取り掛かっている.

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  • 多元ヒト脳図譜データベースによる脳深部刺激療法支援システム

    Grant number:17H05299  2017.04 - 2019.03

    日本学術振興会  科学研究費助成事業 新学術領域研究(研究領域提案型)  新学術領域研究(研究領域提案型)

    諸岡 健一

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    Grant amount:\4290000 ( Direct expense: \3300000 、 Indirect expense:\990000 )

    申請者は,世界初の試みである,1体の日本人献体脳を用いて,脳表から組織・細胞構造までの形態解剖・生理情報を内蔵した精緻なデジタル脳図譜}を開発中である.当該研究は,この標準脳図譜を基盤に,1)性別・年齢(時間軸)や疾患(病理軸)などに特異的な多元脳図譜データベースと,2)これに基づき標準脳図譜を変形させ,脳内構造の個人差・疾患特異性に対応した患者固有脳図譜推定法を構築することで,次世代テイラーメイド脳外科手術支援システムの開発を目的とする.
    平成30年度では,複数の脳図譜間の対応付け法を構築した.データベース構築には,脳図譜間の対応付が必要である.ここで,脳図譜は,脳表上や脳内部にある頂点と,それを接続した四面体からなるボリュームモデルである.一方,ヒトの脳形状が異なり,また異なるヒトの脳図譜ボリュームモデルは.必ずしも同一頂点数・パッチ数とは限らない.そこで,脳図譜ボリュームモデルを,形状が単純な目標体に写像する方法を構築した.
    6つの脳のボリュームモデルを使って,平均的な脳形状を有する目標体に写像する実験を行った.ここで,脳ボリュームモデルは,脳表と2つの内部構造(脳室1:側脳室と第3脳室,脳室2:第4脳室)を持つ.いずれの写像においても,提案手法は脳ボリュームモデルを四面体の自己交差を極力少なくしつつ,モデルを目標体に写像できたことを確認した.また,この写像結果から,逆に目標体のボリュームモデルから脳図譜を復元することで,全ての脳図譜を目標体のボリュームモデルで統一的に記述でき,脳図譜間の対応付けが可能となった.

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  • 多元ヒト脳図譜データベースによる脳深部刺激療法支援システム

    2017 - 2018

    文部科学省  科研費・新学術領域研究(公募研究) 

    諸岡健一

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  • Support System for Minially Invasive Surgery Using Real-time Finite Element Method and Organ Information During Surgery

    Grant number:16K00243  2016.04 - 2019.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    Morooka Ken'ichi, Kurazume Ryo

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    Grant amount:\4550000 ( Direct expense: \3500000 、 Indirect expense:\1050000 )

    The purpose of our research project is to construct support system for Minially Invasive Surgery (MIS) using Real-time Finite Element Method and the information about a target organ acquired during surgeries. We studied the fundamental techniques of the support system: 1) a real-time finite element method for estimating the deformation of the target organ by deep neural networks;
    2) a method for detecting surgical instruments from endoscopic images.

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  • 実時間多層有限要素解析と術中生体情報の融合による次世代低侵襲手術支援システム

    2016 - 2018

    日本学術振興会  科研費・基盤(C) 

    諸岡健一

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  • Acquisition and application using geometry big data by multiple robots with high-resolution laser scanner

    Grant number:26249029  2014.04 - 2018.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A)  Grant-in-Aid for Scientific Research (A)

    Kurazume Ryo

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    Grant amount:\40950000 ( Direct expense: \31500000 、 Indirect expense:\9450000 )

    In this research, we developed a 3D scanning system using multiple robots named CPS-VIII, which combines the laser measurement technique using multiple robots called CPS-SLAM and a high-precision 3D laser scanner. This system is able to acquire geometrical big data consisting of trillions of 3D points. The experimental results showed that the accuracy for CPS-VIII is 0.0085 % of the distance traveled, which means the error is 0.0231 m after the robot moved 270.1 m. In addition, we developed large-scale geometrical big data of urban area and surrounding area of Fukuoka city and provided them on the web. Furthermore, we developed space/road categorization techniques for autonomous vehicles and mobile robots, and proposed high precision recognition techniques using convolutional neural networks and the combination of multiple machine learning techniques.

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  • Computer-Aided System for New Therapy and Diagnosis for Scoliosis Using Real-time Stress Estimation

    Grant number:26560262  2014.04 - 2016.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research  Grant-in-Aid for Challenging Exploratory Research

    Morooka Ken'ichi, KUBOTA Kensuke, KURAZUME Ryo, TSUJI Tokuo, IWASHITA Yumi

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    Grant amount:\3640000 ( Direct expense: \2800000 、 Indirect expense:\840000 )

    We have been studying a new therapy and diagnosis for scoliosis based on the observation and analysis of the motion of spine during breathing. The purpose of our research is to construct a computer aided system for the new therapy and diagnosis for scoliosis by a real-time finite element analysis. To achieve this, we have done research about two fundamental techniques: 1) the development of a real-time finite element analysis by using deep neural networks, and 2)the reconstruction of a patient-specific spine shapes from low resolution medial images with much noise.

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  • 高密度レーザスキャナを搭載した群ロボットによるジオメトリビッグデータの取得と活用

    2014 - 2017

    日本学術振興会  科研費・基盤(A) 

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    Grant type:Competitive

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  • 超高精度・実時間応力変形解析による革新的特発性側彎症治療支援システムの開発

    2014 - 2015

    日本学術振興会  科研費・挑戦的萌芽 

    諸岡健一

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    Authorship:Principal investigator  Grant type:Competitive

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  • Deformable Digital Brain Atlas of the Japanese

    Grant number:24390345  2012.04 - 2016.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

    Miyagi Yasushi, MOROOKA Ken'ichi, FUKUDA Takaichi, OKAMOTO Tsuyoshi

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    Grant amount:\12350000 ( Direct expense: \9500000 、 Indirect expense:\2850000 )

    Stereotactic digital brain atlas was developed from the complete serial histological section of the brain of Japanese. Further, the methods of making averaged brain surface model from neuroimages of many healthy volunteers by use of the self-organizing deformable model, mapping with parameterization that preserves the area and angle of patches of brain surface and also estimating the internal structures of a patient brain by deforming a standard brain atlas by the finite element model were developed. Using our stereotactic atlas of Japanese brain, we analyzed the relationship between the simulation of the current field around DBS lead and the target neural structure; namely, the relationships between the therapeutic effect and abnormal current field of DBS which involves short circuit, between the tolerance to DBS and the lead positioning in the internal pallidum of dystonia and between the long-term effect and the lead positioning in the subthalamic nucleus of Parkinson’s disease.

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  • 実時間有限要素解析を用いた超高精度低侵襲手術支援システムの開発

    Grant number:24103708  2012.04 - 2014.03

    日本学術振興会  科学研究費助成事業 新学術領域研究(研究領域提案型)  新学術領域研究(研究領域提案型)

    諸岡 健一

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    Grant amount:\7410000 ( Direct expense: \5700000 、 Indirect expense:\1710000 )

    当該研究は,肝臓内にある腫瘍を除去する腹腔鏡下手術(腹腔鏡下肝切除術)を対象として, 実時間有限要素解析により,術中の肝臓・腫瘍および周辺組織の精緻な変形を再現し,それを基に術者に有益な情報を提供することで,安全・確実な低侵襲手術を支援するシステムの開発を目的とする.
    平成25年度では,立体内視鏡から取得したステレオ画像から,肝臓表面形状を実時間で復元する方法を開発した.一般に,ステレオ画像から形状表面を復元するために,ステレオ画像を構成する左・右画像の対応関係を求める.この対応付けは,注目画素を中心とした窓を設定し,窓内の局所的特徴パターンの類似性に基づく.しかし,肝臓など,人体組織表面のテクスチャは一様であり,似たような局所パターンが多く存在するため,対応付けが複雑になる.
    これに対し,我々は,広域的エッジという新しい特徴量を定義し,これを用いて対応付けを行った.通常のエッジ値は,注目画素の近傍で差分を取って求めるのに対し,広域的エッジ値は,注目画素を含む一行全体の広い領域での差分計算で求められる.これにより,輝度値変化の少ない連続面上において,周囲の輪郭部分の影響を加味した特徴を得ることができる.また,広域的エッジを効率的に算出する方法を構築し,実時間で形状復元が可能となった.実験結果から,従来のステレオ法と比べ,対応点未検出によるデータの欠損を削減し,連続した領域について,より滑らかな視差情報が得られた.

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  • テイラーメイド型日本人脳座標アトラスの開発

    2012 - 2014

    日本学術振興会  科研費・基盤(B) 

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    Grant type:Competitive

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  • 実時間有限要素解析を用いた超高精度低侵襲手術支援システムの開発

    2012 - 2013

    文部科学省  科研費・新学術領域研究(公募研究) 

    諸岡健一

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  • Development of automated multi-dimensional map construction system using multiple mobile robots

    Grant number:23360115  2011.04 - 2014.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

    KURAZUME Ryo, HASEGAWA Tsutomu, MOROOKA Ken'ichi, IWASHITA Yumi

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    Grant amount:\17810000 ( Direct expense: \13700000 、 Indirect expense:\4110000 )

    In this research, we applied the CPS-SLAM (Cooperative Positioning System - Simultaneous Localization and Mapping) technology, which has been developed for digital archive of cultural heritages, for the construction of a precise map for an outdoor mobile robot, and developed a fully automated multi-dimensional map construction system for a robot by a robot. To develop the proposed system, we conducted the following research topics: 1) automatic planning of cooperative positioning system (CPS), 2) development of high precision measurement system, 3) development of mobile platform for long distance measurement, 4) automatic planning of laser measurement strategy, and 5) construction of multi-dimensional environmental map including dynamic change.

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  • Construction of Patient Specific Brain Atlas Using Database of High Quality Human Brain Atlas

    Grant number:23500244  2011 - 2013

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    KEN'ICHI Morooka, MIYAGI Yasushi

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    Authorship:Principal investigator  Grant type:Competitive

    We have generated a Japanese brain atlas. Using the atlas, we have studied the database composed of many atlases of real patients. One of the fundamental techniques for the database construction is to find the correspondence between brain volume models with triangular mesh. Generally, triangular mesh models of the tissue have different number of vertices and different topology. The correspondence problem becomes complex in the case of the tissue with complex shape such as the human brain.
    To solve this problem, we developed a new method for mapping the model onto its target object with arbitrary shapes and topologies.

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  • 多次元環境地図の自動構築を行う群移動ロボットシステムの開発

    2011 - 2013

    日本学術振興会  科研費・基盤(B) 

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  • Gait identification from invisible shadows

    Grant number:23500216  2011 - 2013

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    IWASHITA Yumi, KURAZUME Ryo, MOROOKA Ken'ichi

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    Grant type:Competitive

    Gait is a powerful remote biometric, offering the advantages of identification from a distance, and of being unobtrusive. We proposed a person identification technique that uses information from person's shadow. In the proposed system, we obtain the advantages of multiple viewpoints with a single camera and additional light sources. More specific, we install multiple infrared lights, which have advantages as undetected sensing, to project shadows of a subject on the ground and a camera in the ceiling inside of a building. Shadow areas, which are projections of one's body on the ground by multiple lights, can be considered as body areas captured from different viewpoints; thus, the proposed system is able to capture multiple projections of the body from a single camera. In addition, we extended the proposed system to improve its performance, by introducing a voting-based method. We showed that the proposed system outperformed an existing method with a database consisting of 54 people.

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  • 計算解剖モデルを利用した実時間有限要素解析による次世代低侵襲手術シミュレータ

    2010 - 2011

    文部科学省  科研費・新学術領域研究(公募研究) 

    諸岡健一

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    Authorship:Principal investigator  Grant type:Competitive

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  • 計算解剖モデルを利用した実時間有限要素解析による次世代低侵襲手術シミュレータ

    Grant number:22103511  2010 - 2011

    日本学術振興会  科学研究費助成事業 新学術領域研究(研究領域提案型)  新学術領域研究(研究領域提案型)

    諸岡 健一

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    Grant amount:\5330000 ( Direct expense: \4100000 、 Indirect expense:\1230000 )

    申請者は,これまで実時間で有限要素解析によって内部組織の変形を推定するシステムneuroFEMの研究を進めきた.この研究では,1つの鉗子で肝臓表面を扱うことを前提として,その操作によって起こり得る変形を1つのニューラルネットワーク(以後,NN)で推定する.肝臓は塊と大まかに捉えるとすると,胃は階層状に並ぶ薄膜の集合体であるため,胃全体の大局的動きと手技による局所変形の両方を再現する必要がある.また,切開や切除により,その操作前後の胃の構造,つまり組織モデルの構造が変わる.このような問題を解決するために,平成23年度では,平成23年度で構築した,胃ESDの手技で生じる組織変形を実時間有限要素解析によって再現するシステムを拡張した.
    具体的には,まず,粘膜切除前後における胃全体の変形を推定するneuroFEMを,効率的に構築する手法を開発した.neuroFEMの構築には,「ある初期条件とそれにより変形した胃モデル」という学習データが多数必要であるが,一方学習データの増加に伴い,1つのNN学習できない可能性が高い.そこで,予備実験を通して,大規模データセットから,1つのNNが学習可能なサブデータセットを自動的に選択する手法を開発した.これにより,学習精度が保証されるだけでなく,学習時間の短縮も可能となった.
    また,胃の内壁にある腫瘍の位置を任意に設定することによって,様々な症例での手術シミュレーションが可能となる.そこで,胃全体の変形をneuroFEMで,腫瘍およびその周囲の組織の変形をバネ-質点モデルを用いるハイブリッドな変形推定システムを構築した.

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  • Construction of Stereotactic Brain Atlas of the Japanese

    Grant number:21591873  2009 - 2011

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    MIYAGI Yasushi, MOROOKA Ken-ichi, FUKUDA Takaichi, TOBIMATSU Shozo, YOSHIURA Takashi, KURAOKA Akio, CHEN Xian, HAYANI Takehito, OKAMOTO Tsuyoshi

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    Grant type:Competitive

    Using the digital imaging technique and micro-slicing technique, we have developed a novel method to construct the stereotactic brain atlas of human with the successful preservation of both histological quality and stereotactic accuracy. This method enabled the construction of stereotactic brain atlas of the Japanese.

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  • 超低侵襲治療を実現する半自律動作性CAD統合内視鏡診断治療ロボットシステムの開発

    2009 - 2011

    日本学術振興会  科研費・基盤(A) 

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  • Advanced Support System for Endoscopic Surgery by Estimating Organ Model with Endoscopic Image Sequence

    Grant number:19700422  2007 - 2008

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B)  Grant-in-Aid for Young Scientists (B)

    MOROOKA Kenichi

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    Authorship:Principal investigator  Grant type:Competitive

    当該研究は,対象臓器を肝臓とし,術前に作成した患者の肝臓メッシュモデルと,術中に得られる内視鏡画像列を用いて,精緻な術中肝臓モデルを実時間で推定する手法の構築,およびその処理を組み込んだ次世代内視鏡手術支援システムの開発を行った.

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  • A study for scalable representation of 3D object models and its applications based on information sensitivity

    Grant number:17300033  2005 - 2007

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

    NAGAHASHI Hiroshi, KUMAZAWA Itsuo, AOKI Kota

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    Grant amount:\15960000 ( Direct expense: \15000000 、 Indirect expense:\960000 )

    In this project, we propose a new method for 3D object model representation and its applications based on a concept of objective sensitivity to a 3D shape, which can deal with multi-view and multi-resolution range data, stereo images, texture images and videos. The objective sensitivity means how much information about the object can be obtained, when its 3D shape is presented in a certain level of detail. The work contains the following sub-goals : The first one is to integrate range data measured in different resolutions and from different view points by considering two certainties for each measured point, and then to generate a 3D shape model in a certain resolution level by using mesh adaptations like subdivision and decimation techniques. Some experimental results have proved that the goal has been achieved.
    The second goal is to propose a statistical texture analysis method that can extract some 3D scale factor from a natural image taken in an outdoor scene, where it is not so easy to perform 3D measurement without using an expensive tool such as a laser range finder. The method is based on a hierarchical linear discriminant analysis that can classify some features calculated from higher-order local auto-correlation functions. It has been proved that the method is available for extracting 3D scale factor from texture images. We have also constructed an active stereo vision system that can control panning, tilting and zooming of the camera as an intelligent vision system of a robot. This system can gather some available information of a scene in the local system without any request from outside.
    The third goal is to develop a new cross-parameterization technique between 3D mesh models that can be used in various 3 dimensional Digital Geometry Processings (DGP). The cross-parameterization method proposed is based on a least-square mesh technique and a self-organizing deformable model(SDM) developed by the authors. This technique has enabled us to transfer texture and motion attributes of a 3D model to another one directly, or to generate intermediate models between two 3D mesh models. As a special application of the 3D morphing that presents these intermediate models temporally, we did several psychological experiments where subjects answer their results when they recognize what an intermediate shape presented is. These experiments have shown that their cognitive processes depend on the combination of the source and target object models.
    Finally, as other applications of the proposed method, we have developed a facial enhancement system based on the SDM, a 3D motion synthesis system based on a machine learning and a clustering algorithm, and a 3D video reconstruction system based on a factorization method.

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  • 情報感度特性に基づく3次元物体のスケーラブル表現とその利用に関する研究

    2005 - 2006

    日本学術振興会  科研費・基盤(B) 

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  • Accurate and Fast 3D Image Reconstruction in Fluorescence Microscopy and Automatic labeling of 3D tissue structures

    Grant number:17300061  2005 - 2006

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

    KUMAZAWA Itsuo, NAGAHASHI Hiroshi, MOROOKA Kenichi

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    Grant type:Competitive

    This project has been aiming to evaluate and improve the new three dimensional reconstruction algorithm developed for Fluorescence Microscopy that has been proven effective for artificially generated ideal data in our previous project. In order to evaluate the effectiveness of the algorithm for actual data observed from tissue structures, we corrected actual data using a fluorescence microscope and have conducted comprehensive evaluation using the data under different conditions by changing noise level and a coupled of optical parameters. Through this evaluation, we found that the new algorithm is sensitive to PSF (Point Spread Function) parameters and, affected by non-transparent parts that contradicts the ideal situation that the deconvolution-based reconstruction theory assumes. We have spent the most of our time to solve this problem and concluded that reconstructed results would not be improved as far as using deconvolution-based-algorithm and it is necessary to develop a method that would not assume transparency for the objects. After the evaluation and adding some improvement, we also tried to develop matching algorithms for three dimensional structures between actual tissue structures observed by Fluorescence Microscopy and artificially synthesized three dimensional structures. We have examined an initial three dimensional shape modeled by a polyhedron would converge to the target shape by iteration procedure using a neural network specifically designed for the purpose.

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  • 形状計測と機能認識の並列協調処理による非剛体物体のモデル獲得

    Grant number:15700149  2003 - 2005

    日本学術振興会  科研費・若手(B)  若手研究(B)

    諸岡健一

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    Authorship:Principal investigator  Grant type:Competitive

    本研究では,3次元非剛体物体の機能とそれにより生じる形状変形の因果関係に着目し,形状計測器と機能認識器を有機的に結合したシステムによって,形状情報と機能情報を持つ3次元物体モデルを生成することを目的とする.平成17年度は,ステレオ視による形状計測精度の向上のため,予め与えられた顔に関する情報と,その情報と画像から得られる特徴量を有機的に結合することによる顔形状計測法の構築を目指した.
    まず,計算機に与える顔情報の構築を行った.レンジファインダによって複数人の顔形状データを獲得する.この形状データは,顔の表面上の点データである.顔形状を復元するために,形状変形可能な可変モデルを用いて,そのモデルを点データに当てはめることで顔形状モデルを生成する.本研究では,新たな可変モデルであるActive Mesh Model (AMM)を導入した.これによって,ある点データと,可変モデルの頂点を対応付けることで,顔モデル間の対応関係を容易に得られる.そこで,全顔形状データより,AMMを使って各顔モデルを生成する.複数人の顔形状データの分布を表す固有顔空間と,目尻・目頭・口などの顔の特徴点の分布を表す特徴点固有空間を作成する.
    これら二種類の固有空間を用いたステレオ視による顔形状計測法を構築した.2台のカメラから取得した2枚の画像から,特徴点を抽出し,それを特徴点固有空間へ投影することで,大まかな形状を求める.固有顔空間と,先ほど推定した形状を事前情報としたMAP推定によって,顔形状を推定する.
    上記手法により実験した結果,ステレオ視のみの手法と比べ,形状復元精度の向上が見られた.しかし,形状復元精度が特徴点の数に依存する問題がある.したがって,今後の課題として,少数の特徴点の場合でも頑強な形状推定法の構築が挙げられる.更に,様々な顔の表情に対して本手法を拡張する必要がある.

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  • 動作機能を学習・獲得する3次元物体モデルの研究

    Grant number:15650030  2003 - 2004

    日本学術振興会  科学研究費助成事業 萌芽研究  萌芽研究

    長橋 宏, 諸岡 健一

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    Grant amount:\3300000 ( Direct expense: \3300000 )

    本研究では、『変形拘束条件を持つ物体モデルが、与えられた外環境の中で自律的に面形状を制御し、動作機能を生じさせることで目標地点に到達するプロセスを学習・獲得する』という枠組を持つ新たな3次元物体表現法を構築することを目的とした。通常の3次元モデル表現が静的な物体形状のみを表し、物体の動きに関しては形状表現とは別にスケルトンモデル等を用いて個別に表現が行われているのに対して、本研究で提案するモデル表現法では、物体モデルが置かれる外環境と目標に応じて、物体の形状変形を引き起こす面の制御点の時間的推移の仕方を自律的に学習することができる。そのため、環境が変わっても自律的に適応可能なエージェント型の3次元物体モデル表現となっており、形状変形と与えられた外環境との相互作用を行う機能を持った物体表現となる。本研究では、区分的多項式表現モデルを利用し、ごく限られた数の制御点の時間関数として動きを表現するとともに、3次元外環境空間における物体の変形と移動を評価するための基準を連続体力学の観点から検討し、それらの評価基準に基づいて形状変形プロセスを学習するシステムを実際に作成した。学習法としては、強化学習法の1つであるQ-learningを用いた。複数の形状モデルに対して、面の変形拘束条件を与えた場合の物体の動きの解析を行った結果、いわゆる水中や地上のような異なる外環境においても、本提案手法に基づいて目標点に到達するための面形状の変形プロセスをそれぞれに獲得することができた。さらに、面の変形拘束条件を変えた場合でも、同一環境、同一目標点に到達するための新たな変形プロセスも獲得できることが明らかとなった。

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