@article{oai:kyutech.repo.nii.ac.jp:00003600,
 author = {Kim, Hyungseop and 金, 亨燮 and Itai, Yoshinori and Tan, Joo kooi and タン, ジュークイ and 石川, 聖二 and Ishikawa, Seiji},
 issue = {2},
 journal = {バイオメディカル・ファジィ・システム学会誌, Journal of Biomedical Fuzzy Systems Association},
 month = {Oct},
 note = {近年医療分野では,CTやMRIなどの画像情報を用いた, コンピュータ画像診断に関する研究が盛んに行われている.その中でも,胸部マルチスライスCT画像からのスリガラス状陰影の抽出は重要であるが,スリガラス状陰影の場合,他の結節影などに比べ,淡い濃度値を示すため,単純な処理では抽出が困難である.本論文では,胸部CT画像からの,スリガラス状陰影の抽出を目的とするCADシステムの開発を行う.本稿では,微小なスリガラス状陰影の形状ではなく,主として濃度分布に着目した特徴量を用いる.提案法ではまず,第一段階として原画像より肺野領域を自動抽出する.次に,血管,空気領域を除去し,スライス間相関による,第1次病変部候補領域の抽出を行う.最後に,濃度特徴量を用いた判別分析を行うことにより,最終的な病変部候補領域を得る.提案法を32症例のマルチスライスCT画像セットに適用し,71.7% の平均識別率を得た., Automatic detection of abnormal shadow area on a multi detector CT image is important task under developing a computer aided diagnosis system. Ground glass opacity is one of the important features in lung cancer diagnosis of computer aided diagnosis. It may be seen as diffuse or more often as patchy in distribution taking sometimes a geographic or mosaic distribution. A large number of diseases can be associated with GGO on CT image. We propose a technique for automatic detection of ground glass opacity from the segmented lung regions by computer based on a set of the thoracic CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground glass opacity is classified by correlation distribution on the successive slice from the extracted lung region with respect to the thoracic CT images. Experiment is performed employing 32 thoracic CT image sets and 71.7% of recognition rates were achieved. Some experiment results are shown along with discussion.},
 pages = {57--63},
 title = {濃度特徴を用いた胸部MDCT像からのスリガラス状陰影の自動抽出},
 volume = {10},
 year = {2008},
 yomi = {イシカワ, セイジ}
}