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

Mark P. Wachowiak; Renata Smolikova; Georgia D. Tourassi; Adel S. Elmaghraby

doi:10.1117/12.480867

15 May 2003 Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration

Mark P. Wachowiak, Renata Smolikova, Georgia D. Tourassi, Adel S. Elmaghraby

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480867
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Mark P. Wachowiak, Renata Smolikova, Georgia D. Tourassi, and Adel S. Elmaghraby "Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480867

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