Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration
Mark P. Wachowiak, Renata Smolikova, Georgia D. Tourassi, Adel Said Elmaghraby · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
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.