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

MISC

 257
  • Y Hata, S Kobashi, N Kamiura, M Ishikawa
    INFORMATION PROCESSING IN MEDICAL IMAGING 1230 387-392 1997年  
    This paper proposes an approach of fuzzy logic to 3D MR image segmentation. We show a fuzzy knowledge representation method to represent the knowledge needed to segment the target portions, and apply our method to 3D MR human brain image segmentation. In it we consider position knowledge, boundary surface knowledge and intensity knowledge. They are expressed by fuzzy if-then rules and compiled to a total degree as the measure of segmentation. The degree is evaluated in region growing technique and which segments the whole brain region into the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. The experimental result on 36 MR voxel data shows that our method extracted the portions precisely.
  • S Kobashi, N Kamiura, Y Hata, M Ishikawa
    INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL I 1 711-714 1997年  
    This paper presents a robust automatic threshold finding method for the human brain MR image segmentation. The method is based on fuzzy information granulation shown by Zadeh. The human brain MR image consists of several parts; the gray matter, white matter, cerebrospinal fluid and so on. By treating their parts as the fuzzy granules in the gray level histogram of the image and developing fuzzy matching technique, we can find required thresholds and can segment the brain region from the MR image. An experiment is done on 50 gray level histograms of the human brain MR volumes. To evaluate our method, we extract the brain region using the obtained thresholds. A comparison of the obtained region with canonical atlas images shows that our method find the thresholds of the gray matter and white matter correctly.
  • 小橋 昌司, 上浦 尚武, 畑 豊
    バイオメディカル・ファジィ・システム学会大会講演論文集 10 69-70 1997年  
    In this paper, we show a medical image segmentation for MR angiography images. In it, we introduce a cluster analysis technique aided by fuzzy logic. Our method classifies the clusters into the blood vessels and the others by comparing the intensity histogram with predefined models. In the experimental results, we show the target MIP and volume rendering images of the segmented region. They are useful for evaluating the lesions such as the areury.
  • 小橋 昌司, 森永 法郎, 平野 章二, 上浦 尚武, 畑 豊, 大和 一晴
    電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society 116(11) 1238-1245 1996年10月20日  
  • 森永 法郎, 小橋 昌司, 上浦 尚武, 畑 豊, 大和 一晴
    インテリジェント・システム・シンポジウム講演論文集 = FAN Symposium : fuzzy, artificial intelligence, neural networks and computational intelligence 6 265-266 1996年10月18日  
  • N Morinaga, S Kobashi, N Kamiura, Y Hata, K Yamato
    SOFT COMPUTING IN INTELLIGENT SYSTEMS AND INFORMATION PROCESSING 170-175 1996年  
    The purpose of this paper establishes a method to decompose the brain region into the inherent portions. In it fuzzy inference is used to evaluate what portion each voxel belongs to. We develop a decomposition method based on standard region growing algorithm, which requires the inference results. The comparison of the volumes of our extracted portions with manually measured volumes by a medical doctor shows that on the average, the error rate is 2% for some MRI data.
  • S Kobashi, N Kamiura, Y Hata, K Yamato
    SOFT COMPUTING IN INTELLIGENT SYSTEMS AND INFORMATION PROCESSING 164-169 1996年  
    In the field of medical science, the extraction of the brain regions from MR images is valuable to diagnose: an Alzheimer's disease. We propose here a novel approach to extract the brain region using the fuzzy matching technique. We describe a modeling of the intensity histogram by fuzzy logic and evaluate fuzzy matching techniques for the extraction of the brain region. We develop the extraction algorithm based on a standard region growing technique. An experimental result on 36 MRI data shows that the error rate is 2.4%, on the average, against manually extracted volumes by a medical doctor.

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