Novel spatial interaction prior for Bayesian image segmentation and restoration

The task of image segmentation implies estimation of the number and associated parameters of the classes within an image, and the class label for each image voxel. In this work, an over-segmentation of the data is first obtained using a Bayesian restoration algorithm. The method incorporates a novel spatial interaction prior, in which neighboring voxels can be classified differently so long as the distance between the centroids of their intensity distributions are within a certain extent. The corresponding functional is iteratively minimized using a series of local optimizations for the label field and a half-quadratic algorithm for the restoration. Redundant classes are then grouped in a second step by making use of information obtained in the initial restoration about the degree of affinity or interaction between the classes. The method is demonstrated on MRI images of the head.