Joint registration and segmentation of dynamic cardiac perfusion images using MRFs

In this paper we propose a Markov random field (MRF) based method for joint registration and segmentation of cardiac perfusion images, specifically the left ventricle (LV). MRFs are suitable for discrete labeling problems and the labels are defined as the joint occurrence of displacement vectors (for registration) and segmentation class. The data penalty is a combination of gradient information and mutual dependency of registration and segmentation information. The smoothness cost is a function of the interaction between the defined labels. Thus, the mutual dependency of registration and segmentation is captured in the objective function. Sub-pixel precision in registration and segmentation and a reduction in computation time are achieved by using a multiscale graph cut technique. The LV is first rigidly registered before applying our method. The method was tested on multiple real patient cardiac perfusion datasets having elastic deformations, intensity change, and poor contrast between LV and the myocardium. Compared to MRF based registration and graph cut segmentation, our method shows superior performance by including mutually beneficial registration and segmentation information.