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There are two main issues that make nonrigid image registration a challenging task. First, voxel intensity similarity may not be necessarily equivalent to anatomical similarity in the image correspondence searching process. Second, during the imaging process, some interferences such as unexpected rotations of input volumes and monotonic gray-level bias fields can adversely affect the registration quality. In this paper, a new feature-based nonrigid image registration method is proposed. The proposed method is based on a new type of image feature, namely, uniform spherical region descriptor (USRD), as signatures for each voxel. The USRD is rotation and monotonic gray-level transformation invariant and can be efficiently calculated. The registration process is therefore formulated as a feature matching problem. The USRD feature is integrated with the Markov random field labeling framework in which energy function is defined for registration. The energy function is then optimized by the α-expansion algorithm. The proposed method has been compared with five state-of-the-art registration approaches on both the simulated and real 3-D databases obtained from the BrainWeb and Internet Brain Segmentation Repository, respectively. Experimental results demonstrate that the proposed method can achieve high registration accuracy and reliable robustness behavior.