NDLI: Multimodal image registration using stochastic differential equation optimization

Content Provider	IEEE Xplore Digital Library
Author	Vegh, V. Zhengyi Yang Tieng, Q.M. Reutens, D.C.
Copyright Year	2010
Description	Author affiliation: Centre for Advanced Imaging, University of Queensland, Brisbane, Australia (Vegh, V.; Zhengyi Yang; Tieng, Q.M.; Reutens, D.C.)
Abstract	An approach to image registration is outlined based on a new stochastic differential equation optimization method. The proposed method requires the use of the numerical solution of a particular stochastic differential equation to determine the iterative update of the transformation variables. A comparison to Differential Evolution optimization was carried out to establish the rate of convergence and the quality of result, as measured by the number of cost function evaluations and the size of the standard deviation of the optimal variables. Experimental data shows that the new technique is robust in terms of computational speed and convergence. The method is validated on magnetic resonance and histology images of mouse brain.
Starting Page	4385
Ending Page	4388
File Size	636020
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5653395
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image registration Optimization Measurement Entropy Convergence Differential equations Joints normalized mutual information multimodal global optimization
Content Type	Text
Resource Type	Article

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