Multimodality image alignment using information-theoretic approach

Authors:
Mohammed Khader;A. Ben Hamza;Prabir Bhattacharya
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC, Canada;Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC, Canada;Department of Computer Science, University of Cincinnati, OH
Venue:
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Year:
2010

Citing 6
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Abstract

In this paper, an entropic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entopy-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.