Single- and multimodal subvoxel registration of dissimilar medical images using robust similarity measures
Medical Imaging 1998: Image Processing
Although a large variety of image registration methods have been described in the literature, only a few approaches have attempted to address the rigid registration of medical images showing gross dissimilarities (due for instance to lesion evolution). In the present p a p e r , w e d e v elop data driven registration algorithms, relying on robust pixel similarity metrics, that enable an accurate (subvoxel) rigid registration of dissimilar single or multimodal 2D/3D images. In the proposed
... ach, gross dissimilarities are handled by considering similarity measures related to robust M-estimators. A \soft redescending" estimator (the Geman-McClure -function) has been adopted to reject gross image dissimilarities during the registration. The registration parameters are estimated using a top down stochatic multigrid relaxation algorithm. Thanks to the stochastic multigrid strategy, the registration is not a ected by local minima in the objective function and a manual initialization near the optimal solution is not necessary. The proposed robust similarity metrics compare favourably to the most popular standard similarity metrics, on patient image pairs showing gross dissimilarities. Two case studies are considered : the registration of MR/MR and MR/SPECT image volumes of patients su ering from multiple sclerosis and epilepsy.