Maximum distance-gradient for robust image registration

To make up for the lack of concern on the spatial information in the conventional mutual information based image registration framework, this paper designs a novel spatial feature field, namely the maximum distance-gradient (MDG) vector field, for registration tasks. It encodes both the local edge information and globally defined spatial information related to the intensity difference, the distance, and the direction of a voxel to a MDG source point. A novel similarity measure is proposed as the combination of the multi-dimensional mutual information and an angle measure on the MDG vector field. This measure integrates both the magnitude and orientation information of the MDG vector field into the image registration process. Experimental results on clinical 3D CT and T1-weighted MR image volumes show that, as compared with the conventional mutual information based method and two of its adaptations incorporating spatial information, the proposed method can give longer capture ranges at different image resolutions. This leads to more robust registrations. Around 2000 randomized rigid registration experiments demonstrate that our method consistently gives much higher success rates than the aforementioned three related methods. Moreover, it is shown that the registration accuracy of our method is high.