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

Ronald W. K. So, Albert C. S. Chung
2011 2011 18th IEEE International Conference on Image Processing  
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
more » ... 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.
doi:10.1109/icip.2011.6116652 dblp:conf/icip/SoC11 fatcat:qltgxyzlkjeefo2uphiteejvsy