Learning-based non-rigid image registration using prior joint intensity distributions with graph-cuts

Non-rigid image registration is widely used in medical image analysis and processing. We recently proposed a novel learning-based similarity measure for non-rigid image registration. The novel similarity measure is constructed by using two Kullback-Leibler distances (KLD), which are based on the a priori knowledge of the joint intensity distribution of a pre-aligned image pair. In this paper, we propose a new formulation for the novel KLD based similarity measure such that it can be exploited in Markov random field (MRF) based non-rigid registration framework with the graph-cuts algorithm. We have compared the proposed formulation against two other similarity measures under the same MRF-based framework, and two state-of-the-art approaches. According to the experimental results, it is demonstrated that the proposed method can achieve high registration accuracy.