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.
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Gan, R., Chung, A.C.S. (2005). Multi-dimensional Mutual Information Based Robust Image Registration Using Maximum Distance-Gradient-Magnitude. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_18
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DOI: https://doi.org/10.1007/11505730_18
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