Although considered as a natural process for human beings, the task of extracting objects from images through segmentation is still requiring a tremendous attention in the computer vision community. Segmentation consists in extracting regions of interest out of images. In medical imaging, the goal of segmentation is to extract anatomical structures of interest such as organs, bones and tissues. Model-based image segmentation is a very common and efficient technique used for segmentation. A mesh is first initialized into the image and then deformed using forces that attract the mesh to fit regions of interest. Those generated meshes may be used both for therapy planning and quantification for diagnosis.

In this thesis, a novel appearance prior for model-based image segmentation in medical imaging is presented. This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon a classification of intensity profiles with model order selection to automatically select the number of profile classes. Unlike classical PCA-based approaches, the clustering is considered as regional because intensity profiles are classified for each mesh and not for each vertex.