Learning-Based Deformation Estimation for Fast Non-Rigid Registration

Abstract

This paper presents a learning-based deformation estimation method for fast non-rigid registration. First, a PCA-based statistical deformation model is constructed using the deformation fields obtained by conventional registration algorithms between a template image and training subject images. Then, the constructed statistical model is used to generate a large number of sample deformation fields by resampling in the PCA space. In the meanwhile, by warping the template using these sample deformation fields, the respective sample images in the PCA space can be also generated. Finally, after learning the correlation between the features of the sample images and their deformation coefficients, given a new test image, we can immediately estimate its relative deformations to the template based on its image information. Using this estimated deformation, we can warp the template to generate an intermediate template close to the test image. Since the intermediate template is more similar to the test image compared to the original template, the deformable registration via the intermediate template becomes much easier and faster. Experimental results show that the proposed learning-based registration method can fast register MR brain image with robust performance.