期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 5, 页码 5548-5556出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.11.064
关键词
Image registration; Tsallis entropy; Stochastic optimization
In this paper, an information-theoretic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entropy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. This divergence measure achieves its maximum value when the conditional intensity probabilities of the transformed target image given the reference image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed approach in comparison to existing entropic image alignment techniques. The feasibility of the proposed algorithm is demonstrated on medical images from magnetic resonance imaging, computer tomography, and positron emission tomography. (C) 2011 Elsevier Ltd. All rights reserved.
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