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Proceedings Paper

Global optimization of weighted mutual information for multimodality image registration
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Paper Abstract

Failure to align images accurately often is due to the optimization algorithms being trapped in local maxima or spurious global maxima of the mutual information function. Strategies contemplated to improve registration involve modifying the optimization scheme or the registration measure itself. We recently found that normalized mutual information (for 2D image registration) provides a larger capture range and that is more robust, with respect to the optimization parameters, than the non-normalized measure. In this paper we assessed the utility of a stochastic global optimization technique for image registration using normalized and non-normalized mutual information. By conducting large-scale studies with patient data in 2D, we established a success rate baseline with the local optimizer only. Formal proof has not yet been found that incorporating the global optimizer does not impair performance. However, experiments to date indicate that its inclusion leads to better (i.e., higher probability of correct convergence) overall performance. More over, studies now underway show good effectiveness of our approach in a variety of 3D cases.

Paper Details

Date Published: 21 May 1999
PDF: 8 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348647
Show Author Affiliations
Claudia E. Rodriguez-Carranza, George Washington Univ. (United States)
Murray H. Loew, George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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