A new statistically-constrained deformable registration framework for MR brain images

Abstract

Statistical models of deformations (SMD) capture the variability of deformations from the template image onto a group of sample images and can be used to constrain the traditional deformable registration algorithms to improve their robustness and accuracy. This paper employs a wavelet-PCA-based SMD to constrain the traditional deformable registration based on the Bayesian framework. The template image is adaptively warped by an intermediate deformation field generated based on the SMD during the registration procedure, and the traditional deformable registration is performed to register the intermediate template image with the input subject image. Since the intermediate template image is much more similar to the subject image, and the deformation is relatively small and local, it is less likely to be stuck into undesired local minimum using the same deformable registration in this framework. Experiments show that the proposed statistically-constrained deformable registration framework is more robust and accurate than the conventional registration.