Multimodal Prior Appearance Models based on Regional Clustering of Intensity Profiles

François Chung (1) , Hervé Delingette (1)

1 ASCLEPIOS - Analysis and Simulation of Biomedical Images

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.

Domaines: Imagerie médicale; Modélisation et simulation; Imagerie; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

François Chung, Hervé Delingette. Multimodal Prior Appearance Models based on Regional Clustering of Intensity Profiles. Medical Image Computing and Computer-Assisted Intervention (MICCAI'09), 2009, London, United Kingdom. pp.1051--1058, ⟨10.1007/978-3-642-04271-3_127⟩. ⟨inria-00616132⟩