Local statistical deformation models for deformable image registration

Abstract

Abstract A fast and robust image registration algorithm for high-dimensional brain Magnetic Resonance images was developed based on the statistical deformation models (SDMs). This model learns deformation fields and achieves fast and robust registration by greatly reducing transformation dimensionality. This model is trained via principal component analysis (PCA), which suffers from large transformation dimensionality and small samples. For the high-dimensional image registration, the dimensions of the deformation fields are huge, the basic functions computed from PCA cannot represent deformation fields well. Therefore, we proposed a local SDM (LSDM) in this paper to solve the aforementioned problems. We divided the images into several small parts, in which the dimensions of the deformation fields are greatly reduced. Then, we trained the LSDM using the deformation fields between sample images and a selected template by applying PCA in each small part. Given that the dimension of eigenvectors of LSDM decreases much more than that of SDM, the orthonormal basis functions of LSDM represent the deformation fields more accurately than those of SDM. We obtained the total deformation fields for warping the image by integrating the deformation fields of all LSDMs. Using the manually labeled MR images of different people, we demonstrated that LSDM could greatly reduce the image registration time while maintaining favorable registration accuracy.