Nonrigid registration combining global and local statistics

In this paper we exploit normalized mutual information for the nonrigid registration of multimodal images. Rather than assuming that image statistics are spatially stationary, as often done in traditional information-theoretic methods, we take into account the spatial variability through a weighted combination of global normalized mutual information and local matching statistics. Spatial relationships are incorporated into the registration criterion by adoptively adjusting the weight according to the strength of local cues. With a continuous representation of images and Parzen window estimators, we have developed closed-form expressions of the first-order variation with respect to any general, nonparametric, infinite-dimensional deformation of the image domain. To characterize the performance of the proposed approach, synthetic phantoms, simulated MRIs, and clinical data are used in a validation study. The results suggest that the augmented normalized mutual information provides substantial improvements in terms of registration accuracy and robustness.