研究者業績

小橋 昌司

コバシ ショウジ  (Syoji Kobashi)

基本情報

所属
兵庫県立大学 工学研究科 教授 (研究所長)
学位
博士(工学)(姫路工業大学)

研究者番号
00332966
ORCID ID
 https://orcid.org/0000-0003-3659-4114
J-GLOBAL ID
200901031674454407
researchmap会員ID
6000003807

外部リンク

論文

 308
  • T Matsuura, S Kobashi, Y Hata
    KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2 69 436-440 2001年  査読有り
    Image segmentation is one of the fundamental techniques to develop a computer-aided diagnosis (CAD) system in the medical field. This paper first introduces rough sets into image segmentation method. In this method, attribute values of each pixel of an image of interest are given by using K-means clustering, and the attribute values divide the image into many regions. By applying value reduct, which is one of the typical concepts of rough sets, to the attribute values, dissimilarities between regions are calculated. Final clustering result is obtained by merging similar regions. To evaluate the performance of the proposed image segmentation method, it was applied to an artificial generated image, and a human brain Magnetic Resonance (MR) image. The results were also compared with convention K-means clustering.
  • 畑豊, 小橋昌司, 喜多村祐里, 柳田敏雄
    Medical Imaging Technology 2000年4月  
  • Syoji Kobashi, Yutaka Hata, Yuri T. Kitamura, Toshio Yanagida
    Biomedical Soft Computing and Human Sciences 6(1) 85-94 2000年4月  
    This paper proposes an image segmentation method based on fuzzy if-then rules. It is a derivative of the conventional region growing method. This method represents expert's knowledge using fuzzy if-then rules, and embeds them as the growing criteria. To examine the proposed method, it has been applied to artificially generated images involving white Gaussian noise. In comparison with the conventional region growing method, the proposed method can segment region of interests(ROIs)with high robustness against to white noise. Moreover, it has been applied to dynamic mognetic resonance(MR)images of the Liver. The growing Criteria that represent physician's knowledge of MR images were derivedfrom the illustrated time-density curve of the liver, hepatic arteries, and veins after intravenous bolus injection. The experiments were done on three different normal volunteer with promising results.
  • Syoji Kobashi, Yutaka Hata, Yuri T. Kitamura, Toshio Yanagida
    Proceedings of 4th Asian Fuzzy Systems Symposium 2000年4月  
  • Y Hata, S Kobashi, S Hirano
    1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5 4098-4103 1998年  査読有り
    This paper describes useful fuzzy logic techniques for medical image segmentation. Specific methods to be reviewed include fuzzy information granulation, fuzzy inference and fuzzy cluster identification. Fuzzy information granulation is introduced as a powerful scheme to find the thresholds to obtain the whole brain region in MR data. Fuzzy inference technique succeeds to segment the brain region into the left cerebral hemisphere, right cerebral hemisphere, cerebellum and brain stem. The fuzzy inference aided segmentation procedure is also useful to human foot CT image. Fuzzy cluster identification is adapted to determine the obtained clusters into blood vessel or other tissues in MRA image.
  • E Mori, M Yasuda, H Kitagawa, S Kobashi, Y Hata
    CAR '98 - COMPUTER ASSISTED RADIOLOGY AND SURGERY 1165 82-87 1998年  査読有り
  • H Kitagaki, E Mori, K Ishii, S Kobashi, Y Hata
    CAR '98 - COMPUTER ASSISTED RADIOLOGY AND SURGERY 1165 76-81 1998年  査読有り
  • 北垣 一, 山路 滋, 石井 一成, 森 悦朗, 小橋 昌司, 畑 豊
    日本磁気共鳴医学会雑誌 17(Suppl.) 103-103 1997年9月  

MISC

 257

講演・口頭発表等

 214

担当経験のある科目(授業)

 17

共同研究・競争的資金等の研究課題

 25

学術貢献活動

 5

社会貢献活動

 2

メディア報道

 11