Multimodal Prior Appearance Models Based on Regional Clustering of Intensity Profiles

Model-based image segmentation requires prior information about the appearance of a structure in the image. Instead of relying on Principal Component Analysis such as in Statistical Appearance Models, we propose a method based on a regional clustering of intensity profiles that does not rely on an accurate pointwise registration. Our method is built upon the Expectation-Maximization algorithm with regularized covariance matrices and includes spatial regularization. The number of appearance regions is determined by a novel model order selection criterion. The prior is described on a reference mesh where each vertex has a probability to belong to several intensity profile classes.

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: François Chung, Hervé Delingette
• Keywords: Medical Imaging, MICCAI 2009, Model-based Image Segmentation, Regularized Covariance Matrices, Statistical Appearance Models