Nonrigid Image Registration Using DynamicHigher-Order MRF Model

Abstract

In this paper, we propose a nonrigid registration method using the Markov Random Field (MRF) model with a higher-order spatial prior. The registration is designed as finding a set of discrete displacement vectors on a deformable mesh, using the energy model defined by label sets relating to these vectors. This work provides two main ideas to improve the reliability and accuracy of the registration. First, we propose a new energy model which adopts a higher-order spatial prior for the smoothness cost. This model improves limitations of pairwise spatial priors which cannot fully incorporate the natural smoothness of deformations. Next we introduce a dynamic energy model to generate optimal displacements. This model works iteratively with optimal data cost while the spatial prior preserve the smoothness cost of previous iteration. For optimization, we convert the proposed model to pairwise MRF model to apply the tree-reweighted message passing (TRW). Concerning the complexity, we apply the decomposed scheme to reduce the label dimension of the proposed model and incorporate the linear constrained node (LCN) technique for efficient message passings. In experiments, we demonstrate the competitive performance of the proposed model compared with previous models, presenting both quantitative and qualitative analysis.