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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2017-01-18 15:48 |
Okinawa | Tenbusu Naha | [Short Paper]
PET image analysis using sparse nonnegative matrix factorization for accumulation model survey of amyloid beta Tatsuya Nagata, Hidekata Hontani, Tatsuya Yokota (NIT), Yuchi Kimura (Kindai Univ.), Koichi Ito (Tohoku Univ.), Takashi Kato, Kaori Iwata, Akinori Nakamura (NCGG)  MI2016-117 |
(To be available after the conference date) [more] | MI2016-117 pp.183-184 |
| MI | 2016-11-14 15:00 |
Tottori | Tottori Univ. | [Short Paper]
Study of Dynamic PET Reconstruction by Low-rank Non-negative Matrix Factorization Junya Yamada, Hidekata Hontani, Tatsuya Yokota (NIT), Muneyuki Sakata (TMIG), Yuichi Kimura (KU) MI2016-69 |
Tissue time activity curves observed in dynamic images are used for kinetic analysis of brain PET. In order for improvin... [more] | MI2016-69 pp.31-32 |
| MI | 2016-07-26 15:25 |
Hokkaido | Tomakomai Civic Hall | Super Resolution of MR images via TV-minimization in Signal Space and Spectrum Fitting in Fourier Space Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani (NIT), Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume (Kyushu Univ) MI2016-46 |
In usual MRI, one of three directions of spacial resolution is lower than other directions, with their slices shifted. V... [more] | MI2016-46 pp.55-60 |
| PRMU, IE, MI, SIP | 2016-05-19 16:10 |
Aichi | Large Deformation Diffeomorphic Metric Mapping of Figures with Global Features Shuntaro Monobe, Hidekata Hontani (NIT) SIP2016-10 IE2016-10 PRMU2016-10 MI2016-10 |
[more] | SIP2016-10 IE2016-10 PRMU2016-10 MI2016-10 pp.49-52 |
|
| PRMU, IE, MI, SIP | 2016-05-20 15:40 |
Aichi | Highly Accurate Reconstruction of Microscope 3D images based on Micro Anatomic Structures Hirokazu Kobayashi, Hidekata Hontani (NIT) SIP2016-29 IE2016-29 PRMU2016-29 MI2016-29 |
The objective of this research is to develop a method for accurately registering 2D microscope images of spatially succ... [more] |
SIP2016-29 IE2016-29 PRMU2016-29 MI2016-29 pp.153-156 |
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| PRMU, IE, MI, SIP | 2016-05-20 16:10 |
Aichi | Analysis of 3D Periodic Network Structures of Liver Bile Canaliculus in CLSM Images Shogo Sakuma, Hidekata Hontani (NITech), Hiroto Shoji (KPUM) SIP2016-30 IE2016-30 PRMU2016-30 MI2016-30 |
The objective of this research is to develop a method for analyzing 3D graph structures of bile canaliculus networks in... [more] |
SIP2016-30 IE2016-30 PRMU2016-30 MI2016-30 pp.157-160 |
|
| MICT | 2016-03-09 15:55 |
Aichi | Nagoya Institute of Technology | [Invited Talk]
A cross section of multidisciplinary computational anatomy Hidetaka Hontani (NIT) MICT2015-58 |
[more] | MICT2015-58 p.23 |
| MI | 2016-01-19 17:30 |
Okinawa | Bunka Tenbusu Kan | Report on MICCAI 2015 Shouhei Hanaoka (Tokyo Univ.), Kenichi Karasawa (Nagoya Univ.), Hidekata Hontani (Nagoya Institute of Tech.), Xiangrong Zhou (Gifu Univ.), Yasushi Hirano (Yamaguchi Univ.), Masahiro Oda (Nagoya Univ.), Yoshitaka Masutani (Hiroshima City Univ.), Akinobu Shimizu (Tokyo Univ. of Agriculture and Technology), Kensaku Mori (Nagoya Univ.) MI2015-110 |
[more] | MI2015-110 pp.177-182 |
| MI | 2015-09-08 14:50 |
Tokyo | Univ. of Electro-communications | Model Selection from the q-Exponential Distribution for Constructing an Organ Point Distribution Model Mitsunori Yamada, Hidekata Hontani, Hiroshi Matsuzoe (NIT) MI2015-54 |
The authors propose a method that selects an appropriate distribution model for accurately representing statistics of th... [more] | MI2015-54 pp.31-34 |
| MI | 2015-07-14 13:30 |
Hokkaido | Sun Refle Hakodate | Automatic Artery Extraction in Ultrasound Images for the aid of FMD test Kaori Nomura (NIT), Hiroshi Masuda (UNEX), Kazumasa Sano (Toe), Toshihiko Koyama (DENSO), Hidenori Suzuki (UNEX), Hidekata Hontani (NIT) MI2015-32 |
The authors propose a method for automatically extracting and tracking an artery region in a ultrasound temporal image ... [more] |
MI2015-32 pp.1-6 |
| MI | 2015-07-14 15:10 |
Hokkaido | Sun Refle Hakodate | Study on Description of Contour Structures in Pathological Images for their Registration Naoki Kawamura, Hidekata Hontani (NIT), Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume (Kyushu univ.) MI2015-35 |
In this article, the authors propose a method that describes the structures of contours in pathological images of the p... [more] |
MI2015-35 pp.19-22 |
| MI | 2015-07-14 16:20 |
Hokkaido | Sun Refle Hakodate | Advances of Computer vision and progress of medical image recognition and understanding
-- Panel discussion -- Hidekata Hontani (NITech), Yoshitaka Masutani (Hiroshima City Univ.), Yoshinobu Sato (NAIST), Akinobu Shimizu (TUAT), Kensaku Mori (Nagoya Univ.) MI2015-37 |
In this article, the authors discuss a history of computer vision, that of a medical image recognition and nature of med... [more] | MI2015-37 pp.27-32 |
| MI | 2015-03-02 10:54 |
Okinawa | Hotel Miyahira | Image Feature Extraction and Construction of a Classifier for Discriminating Pulmonary Nodules in X-CT Images Ryuta Mori, Takumi Naito, Hidekata Hontani (NIT), Shingo Iwano (Nagoya Univ.) MI2014-61 |
In this article, the authors report about a method for classifying pulmonary nodules in three-dimensional X-CT images an... [more] | MI2014-61 pp.45-48 |
| MI | 2015-03-02 16:24 |
Okinawa | Hotel Miyahira | Segmentation of Organ Regions in Medical Images using Hierarchical Active Shape Models Shohei Kamiya, Hidekata Hontani (NIT) MI2014-85 |
In this article, the authors report on a hierarchical parametric Active Shape Model (ASM). Different from conventional A... [more] | MI2014-85 pp.147-150 |
| MI | 2015-03-02 16:36 |
Okinawa | Hotel Miyahira | Eliminations of dura maters from MR Images using probablistic atlas and graph cut technique Shin-ya Uto, Hidekata Hontani (NIT), Muneyuki Sakata (TMIG), Yuichi Kimura (Kinki Univ) MI2014-86 |
This study aims at proposing algorithm that liminate the dura maters in brain MR images accurately. Ordinary methods oft... [more] | MI2014-86 pp.151-155 |
| MI | 2014-09-02 10:45 |
Tokyo | The Institute of Statistical Mathematics | A Study on Preprocessing for Organ Region Segmentation
-- Application of superpixels and of saliency detection -- Yushi Deura, Naoki Kawamura, Mitsunori Yamada, Hidekata Hontani (Nagoya Inst. of Tech.) MI2014-36 |
In this study, the authors study preprocessing for segmenting organ regions in medical images. Many researches of organ... [more] |
MI2014-36 pp.7-11 |
| PRMU, MI, IE, SIP | 2014-05-22 15:10 |
Aichi | Window Size Estimation for Parametrically Describing Local Image Appearances by means of the Weighted Integral Method Hidetoshi Goto, Hidekata Hontani (Nagoya Inst. of Tech.) SIP2014-9 IE2014-9 PRMU2014-9 MI2014-9 |
The authors propose a new method that accurately estimates the parameters of the Gaussian function that describes a loca... [more] | SIP2014-9 IE2014-9 PRMU2014-9 MI2014-9 pp.43-48 |
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| MI | 2014-01-26 16:05 |
Okinawa | Bunka Tenbusu Kan | Registration of Organs for Partial Medical Images Using Anatomical Landmarks Yoshiki Kamiya, Naoki Kawamura, Hidekata Hontani (Nagoya Inst. of Tech.) MI2013-85 |
In this manuscript, the authors report about a method that registers a statistical shape model of a target organ to gi... [more] |
MI2013-85 pp.161-164 |
| MI | 2014-01-26 16:20 |
Okinawa | Bunka Tenbusu Kan | Selection of Landmarks Appropriate for Body Shape Normalization in Organ Registration Yoshihiro Tsutsumi, Hidekata Hontani (Nagoya Inst. of Tech.) MI2013-86 |
In this paper, the authors propose a method for selecting appropriate anatomical landmarks used for normalizing body sh... [more] | MI2013-86 pp.165-168 |
| MI | 2014-01-27 13:30 |
Okinawa | Bunka Tenbusu Kan | Comparison between a Renormalization Method for Linear Regression and a Bayes Method for Denoizing with respect to Improvement of Dynamic PET Image Analysis Takahiro Kozawa, Hidekata Hontani (Nagoya Inst. of Tech.), Kazuya Sakaguchi (Kitasato Univ), Muneyuki Sakata (TMIG), Yuichi Kimura (Kinki Univ) MI2013-109 |
The tissue time activity curve (tTAC) represents a time variation of radioactivity of ligand in the brain derived from b... [more] | MI2013-109 pp.285-290 |
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