SUMMARYIn recent years, there has been increasing interest in statistical shape modeling of human anatomy. The statistical shape model can capture the morphological variations of human anatomy. Since 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 the marching cubes algorithm to convert the segmented liver volume to a triangulated mesh surface containing 1000 vertex points. The coordinates of these vertex points are used to represent the 3D liver shape as a shape vector. After normalization and identification of correspondences between all datasets, principal component analysis (PCA) is employed to find the principal variation modes of the shape vectors. Then the authors propose a mode selection method based on class variations between the normal class and abnormal class. The authors found that the top two modes of class variations are most effective for the classification of normal and abnormal livers. The classification rate of abnormal livers and normal livers by the use of a simple linear discriminant function were 84% and 80%, respectively. C⃝ 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 190(4): 37-45, 2015; Published online in Wiley Online Library (wileyonlinelibrary.com).

SUMMARY In recent years, there has been increasing interest in statistical shape modeling of human anatomy. The statistical shape model can capture the morphological variations of human anatomy. Since 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 the marching cubes algorithm to convert the segmented liver volume to a triangulated mesh surface containing 1000 vertex points. The coordinates of these vertex points are used to represent the 3D liver shape as a shape vector. After normalization and identification of correspondences between all datasets, principal component analysis (PCA) is employed to find the principal variation modes of the shape vectors. Then the authors propose a mode selection method based on class variations between the normal class and abnormal class. The authors found that the top two modes of class variations are most effective for the classification of normal and abnormal livers. The classification rate of abnormal livers and normal livers by the use of a simple linear discriminant function were 84% and 80%, respectively.

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Abstract-In the fields of medical image analysis and computational anatomy, statistical shape models (SS Ms) is usually used for organ segmentation; SS Ms are statistically constructed from a population of organs. In this paper, we focus on the application of SS Ms for the computer-aided diagnosis of cirrhotic livers. Since chronic liver diseases or cirrhosis will cause significant morphological changes on both the liver and spleen, we constructed multiple SS Ms (i.e., liver SS M, spleen SS M, and a joint SSM of the liver and spleen) for morphological analysis. Coefficients of SS Ms are used as features for the classification of normal and cirrhotic livers. Through this paper, we show that classification accuracy can be significantly improved by effective mode selection, which is based on fisher discriminant analysis, and the use of a non-linear support vector machine. Furthermore, we also construct Computer-aided Diagnosis (CAD) of liver cirrhosis system using SSMs.