Spectral Demons - Image Registration via Global Spectral Correspondence

Herve Lombaert, Leo Grady, Xavier Pennec, Nicholas Ayache, Farida Cheriet

Abstract: Image registration is a building block for many applications in computer vision and medical imaging. However the current methods are limited when large and highly non-local deformations are present. In this paper, we introduce a new direct feature matching technique for non-parametric image registration where efficient nearest-neighbor searches find global correspondences between intensity, spatial and geometric information. We exploit graph spectral representations that are invariant to isometry under complex deformations. Our direct feature matching technique is used within the established Demons framework for diffeomorphic image registration. Our method, called Spectral Demons, can capture very large, complex and highly non-local deformations between images. We evaluate the improvements of our method on 2D and 3D images and demonstrate substantial improvement over the conventional Demons algorithm for large deformations.