肝臓の統計形状モデル構築と肝硬変症支援診断システムへの応用

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2013/11/01

タイトル(英語): Statistical Shape Model of the Liver and Its Application to Computer Aided Diagnosis of Liver Cirrhosis

著者名: 上谷　芽衣（立命館大学 情報理工学部），健山　智子（立命館大学 情報理工学部），小原　伸哉（立命館大学 情報理工学部），田中　英俊（立命館大学 情報理工学部），韓　先花（立命館大学 情報理工学部），金崎　周造（京都武田病院），古川　顕（首都大学東京 健康福祉学部），陳　延偉（立命館大学 情報理工学部）

著者名(英語): Mei Uetani (Department of Science and Engineering, Ritsumeikan University), Tomoko Tateyama (Department of Science and Engineering, Ritsumeikan University), Shinya Kohara (Department of Science and Engineering, Ritsumeikan University), Hidetoshi Tanaka (Department of Science and Engineering, Ritsumeikan University), Xian-hua Han (Department of Science and Engineering, Ritsumeikan University), Shuzo Kanasaki (Kyoto Takeda Hospital), Akira Furukawa (Tokyo Metropolitan University), Yen-Wei Chen (Department of Science and Engineering, Ritsumeikan University)

キーワード: 統計形状モデル，肝硬変症，コンピュータ支援診断，有効成分特定，識別関数　　Statistical Shape Model，Liver Cirrhosis，Computer Aided Diagnosis，Effective Mode Selection，Discriminant Function

要約(英語): In recent years, there are increasing interests in statistical shape modeling of human anatomy. The statistical shape model can capture the morphological variations of human anatomy. Since the liver cirrhosis will cause significant morphological changes, the authors propose a computer-aided diagnosis method for liver cirrhosis based on statistical shape models. In the proposed method, the authors first construct a statistical shape model of the liver using 50 clinical CT datasets (25 sets of normal data and 25 sets of abnormal data). The authors apply marching cubes algorithm to convert the segmented liver volume to a triangulated mesh surface which containing 1000 vertex points. The coordinates of these vertex points are used to represent 3D liver shape as a shape vector. After normalization and correspondence finding between all datasets, Principal Component Analysis (PCA) is employed to find the principal variation modes of shape vectors. Then the authors propose a mode selection method based on class variations between the normal class and abnormal class. The authors found the top two modes of class variations are most effective for classification of normal and abnormal livers. The classification rate of abnormal livers and normal liver are 84% and 80%, respectively, by the use of a simple linear discriminant function.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.11 （2013） 特集：電気関係学会関西連合大会

本誌掲載ページ: 2037-2043 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/133/11/133_2037/_article/-char/ja/