Multi-dimensional Mutual Information Based Robust Image Registration Using Maximum Distance-Gradient-Magnitude

In this paper, a novel spatial feature, namely maximum distance-gradient-magnitude (MDGM), is defined for registration tasks. For each voxel in an image, the MDGM feature encodes spatial information at a global level, including both edges and distances. We integrate the MDGM feature with intensity into a two-element attribute vector and adopt multi-dimensional mutual information as a similarity measure on the vector space. A multi-resolution registration method is then proposed for aligning multi-modal images. Experimental results show that, as compared with the conventional mutual information (MI)-based method, the proposed method has longer capture ranges at different image resolutions. This leads to more robust registrations. Around 1200 randomized registration experiments on clinical 3D MR-T1, MR-T2 and CT datasets demonstrate that the new method consistently gives higher success rates than the traditional MI-based method. Moreover, it is shown that the registration accuracy of our method obtains sub-voxel level and is acceptably high.