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Mutual information has been widely used as a similarity metric for biomedical image registration. Although usually based on the Shannon definition of entropy, mutual information may be computed from other entropy definitions. Mutual information similarity metrics computed from fractional order Renyi entropy and entropy kind t are presented as novel similarity metrics for ultrasound/MRI registration. These metrics are shown to be more accurate than Shannon mutual information in many cases, and frequently facilitate faster convergence to the optimum. They are particularly effective for local optimization, but some measures may potentially be exploited for global searches.