Graph Cuts Segmentation with Statistical Shape Priors for Medical Images

Segmentation of medical images is an important step in many clinical and diagnostic imaging applications. Medical images present many challenges for automated segmentation including poor contrast at tissue boundaries. Traditional segmentation methods based solely on information from the image do not work well in such cases. Statistical shape information for objects in medical images are easy to obtain. In this paper, we propose a graph cuts-based segmentation method for medical images that incorporates statistical shape priors to increase robustness. Our proposed method is able to deal with complex shapes and shape variations while taking advantage of the globally efficient optimization by graph cuts. We demonstrate the effectiveness of our method on kidney images without strong boundaries.