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Utilizing Disease-Specific Organ Shape Components for Disease Discrimination: Application to Discrimination of Chronic Liver Disease from CT Data
Dipti Prasad Mukherjee1, 2, Keisuke Higashiura3, Toshiyuki Okada2, Masatoshi Hori2, Yen-Wei Chen3, Noriyuki Tomiyama2, and Yoshinobu Sato2
1Indian Statistical Institute, Kolkata, West Bengal, India
2Department of Radiology, Graduate School of Medicine, Osaka University, Japan
3Graduate School of Information Science and Engineering, Ritsumeikan University, Japan
Abstract. We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace representing inter-subject shape variability. The first PCA is applied to the datasets of healthy organ shapes to define the subspace of normal variability. Then, the datasets of diseased shapes are projected onto the orthogonal complement (OC) of the subspace of normal variability, and the second PCA is applied to the projected datasets to derive the subspace representing the disease-specific variability. To calculate the OC of an n-dimensional subspace, a novel closed-form formulation is developed. Experiments were performed to show that the support vector machine classification in the OC subspace better discriminated healthy and diseased liver shapes using 99 CT data. The effects of the number of training data and the difference in segmentation methods on the classification accuracy were evaluated to clarify the characteristics of the proposed method.
Keywords: Statistical shape model, orthogonal complement, support vector machine, liver fibrosis, computer-aided diagnosis
LNCS 8149, p. 235 ff.
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© Springer-Verlag Berlin Heidelberg 2013