Medical image registration using stochastic optimization

被引:11
作者
Mohamed, Waleed [1 ]
Ben Hamza, A. [1 ]
机构
[1] Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ EV7 631, Canada
关键词
Image registration; Tsallis entropy; Divergence measure; Stochastic optimization; DIVERGENCE; ENTROPY;
D O I
10.1016/j.optlaseng.2010.06.011
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We propose an image registration method by maximizing a Tsallis entropy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. Due to its convexity property, this divergence measure attains its maximum value when the conditional intensity probabilities between the reference image and the transformed target 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. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1213 / 1223
页数:11
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