A priori information in image segmentation: Energy functional based on shape statistical model and image information

In this paper, we propose an energy functional to segment objects whose global shape is a priori known thanks to a statistical model. Our work aims at extending the variational approach of Chen et al. [1] by integrating the statistical shape model of Leventon et al. [2]. The proposed energy functional allows us to capture an object that exhibits high image gradients and a shape compatible with the statistical model which best fits the segmented object. The minimization of the functional provides a system of coupled equations whose steady-state solution is the solution of the segmentation problem. Results are presented on synthetic and medical images.