Shinya Kohara

Preliminary study on statistical shape model applied to diagnosis of liver cirrhosis

In computational anatomy, statistical shape model (SSM) is used for the quantitative evaluation of variations in the shapes of different organs. This paper focuses on the construction of a SSM of the liver and its application to computer-assisted diagnosis of cirrhosis. We prove the potential application of SSMs in the classification of normal and cirrhotic livers. In constructing a SSM of the liver, we first normalize volume data followed by the construction of the model using principal component analysis. The coefficients of the model are used as indicators of liver pathology. The effectiveness of the constructed model is evaluated by the classification accuracy of both normal and abnormal data.

Efficient shape representation and statistical shape modeling of the liver using spherical harmonic functions (SPHARM)

In the field of medical image analysis, the three-dimensional (3-D) shape representation and modeling of anatomic structures using only a few parameters is an important issue, and can be applied to computer assisted diagnosis, surgical simulations, visualization, and many other medical applications. In this paper, we show that the 3D anatomical structure such as the liver can be represented by a few coefficients of spherical harmonic functions (SPHARM). We also propose to use SPHARM based shape representation for statistical shape modeling. Since the dimension of SPHARM based shape representation vector is much lower than the conventional shape representation using coordinates of surface points, our proposed method can be used for small number of training samples and enhance the computation cost.