A shape constrained MAP-EM algorithm for colorectal segmentation

The task of effectively segmenting colon areas in CT images is an important area of interest in medical imaging field. The ability to distinguish the colon wall in an image from the background is a critical step in several approaches for achieving larger goals in automated computer-aided diagnosis (CAD). The related task of polyp detection, the ability to determine which objects or classes of polyps are present in a scene, also relies on colon wall segmentation. When modeling each tissue type as a conditionally independent Gaussian distribution, the tissue mixture fractions in each voxel via the modeled unobservable random processes of the underlying tissue types can be estimated by maximum a posteriori expectation-maximization (MAP-EM) algorithm in an iterative manner. This paper presents, based on the assumption that the partial volume effect (PVE) could be fully described by a tissue mixture model, a theoretical solution to the MAP-EM segmentation algorithm. However, the MAP-EM algorithm may miss some small regions which also belong to the colon wall. Combining with the shape constrained model, we present an improved algorithm which is able to merge similar regions and reserve fine structures. Experiment results show that the new approach can refine the jagged-like boundaries and achieve better results than merely exploited our previously presented MAP-EM algorithm.