Multi-dimensional mutual information based robust image registration using maximum distance-gradient-magnitude

Authors:
Rui Gan;Albert C. S. Chung
Affiliations:
Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science, The Hong Kong University of Science and Technology, Hong Kong;Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science, The Hong Kong University of Science and Technology, Hong Kong
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

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Abstract

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 informa- tion 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 mea- sure 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 im- age resolutions. This leads to more robust registrations. Around 1200 ran- domized registration experiments on clinical 3D MR-T1, MR-T2 and CT datasets demonstrate that the new method consistently gives higher suc- cess 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.