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Proceedings Paper

Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration
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Paper Abstract

Information theoretic similarity metrics, including mutual information, have been widely and successfully employed in multimodal biomedical image registration. These metrics are generally based on the Shannon-Boltzmann-Gibbs definition of entropy. However, other entropy definitions exist, including generalized entropies, which are parameterized by a real number. New similarity metrics can be derived by exploiting the additivity and pseudoadditivity properties of these entropies. In many cases, use of these measures results in an increased percentage of correct registrations. Results suggest that generalized information theoretic similarity metrics, used in conjunction with other measures, including Shannon entropy metrics, can improve registration performance.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480867
Show Author Affiliations
Mark P. Wachowiak, Univ. of Louisville (United States)
Renata Smolikova, Univ. of Louisville (United States)
Georgia D. Tourassi, Duke Univ. Medical Ctr. (United States)
Adel S. Elmaghraby, Univ. of Louisville (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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