Abstract: Abstract Image segmentation problem often demands the incorporation of as much prior information as possible to help the segmentation algorithms extract the tissue of interest The model of image segmentation based on statistical shape prior level set was reviewed. The feature of mode is the energy function of the model composed by two terms. The first one is data term based on the image gradient or region gray intensity, the second one is the shape prior term which provides robustness against missing shape information due to cluttering,occlusion and gaps. How to construct the implicit shape model which aims to extract a compact representation for the structure of interest from a set of training examples, how to construct the evolve model to constrain an implicit surface to follow global shape consistence while prescrving its ability to capture local deformation were discussed intensively. The key problems such as shape registration and the correspondence problem were introduced. Finally the open issues and possible future research directions were pointed.

Key words: Shape prior, Principal component analysis, Maximum a posteriori(MAP) , Level set