An MRF framework for joint registration and segmentation of natural and perfusion images

Abstract

Registration and segmentation provide complementary information about each other. In this paper we propose a method for the joint registration and segmentation (JRS) of images using Markov random fields (MRFs). The use of MRFs allows us to formulate the problem as one of labeling and apply fast discrete optimization techniques like graph cuts. Graph cuts is able to overcome the limitations of previously used active contour frameworks namely, large number of iterations, risk of being trapped in local minima, and sensitivity to initialization. The labels in the MRF formulation indicate joint occurrence of displacement vectors and segmentation class and the energy formulation is able to capture their mutual dependency. Experiments on real patient perfusion data and natural images show that JRS gives better performance than conventional registration and segmentation methods.