title: Utilizing disease-specific organ shape components for disease discrimination: application to discrimination of chronic liver disease from CT data.
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 sub-space 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.
authors: Indian Statistical Institute, Kolkata, West Bengal, India.
authors: Dipti Prasad
authors: DP
authors: Mukherjee
authors: Graduate School of Information Science and Engineering, Ritsumeikan University, Japan.
authors: Keisuke
authors: K
authors: Higashiura
authors: Department of Radiology, Graduate School of Medicine, Osaka University, Japan.
authors: Toshiyuki
authors: T
authors: Okada
authors: Department of Radiology, Graduate School of Medicine, Osaka University, Japan.
authors: Masatoshi
authors: M
authors: Hori
authors: Graduate School of Information Science and Engineering, Ritsumeikan University, Japan.
authors: Yen-Wei
authors: YW
authors: Chen
authors: Department of Radiology, Graduate School of Medicine, Osaka University, Japan.
authors: Noriyuki
authors: N
authors: Tomiyama
authors: Department of Radiology, Graduate School of Medicine, Osaka University, Japan.
authors: Yoshinobu
authors: Y
authors: Sato
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D003937:Diagnosis, Differential; D058625:End Stage Liver Disease / Q000000981:diagnostic imaging*; D006801:Humans; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*