Statistical shape model of the liver and its application to computer aided diagnosis of liver cirrhosis

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.