Non-rigid Image Registration with Uniform Spherical Structure Patterns

Abstract

Non-rigid image registration is a challenging task in medical image analysis. In recent years, there are two essential issues. First, intensity similarity is not necessarily equivalent to anatomical similarity when the anatomical correspondences between subject and template images are established. Second, the registration algorithm should be robust against monotonic gray-level transformation when aligning anatomical structures in the presence of bias fields. In this paper, a new feature based non-rigid registration method is proposed to deal with these two problems. The proposed method is based on a new type of image feature, called Uniform Spherical Structure Pattern (USSP). USSP encodes voxel-wise interaction information and geometric properties of anatomical structures. It is computationally efficient, rotation invariant and theoretically monotonic gray-level transformation invariant. The USSP feature is integrated with the Markov random field (MRF) discrete labeling framework to define energy function for registration in this paper. If the segmentation results are available, explicit anatomical correspondence can be established as an additional energy term. The energy function is optimized via the alpha-expansion algorithms. The proposed method is compared with three widely used non-rigid registration methods on both simulated and real databases obtained from BrainWeb and IBSR. Experimental results demonstrate that the proposed method achieves the highest registration accuracy among all the compared methods.