Multiresolution Biomedical Image Registration Using Generalized Information Measures

In addition to the widely-used Shannon mutual information, generalized information-theoretic similarity metrics have properties that make them conducive to biomedical image registration. The mutual information based on Havrda-Charvat and R?enyi entropy measures are compared to Shannon mutual information, normalized mutual information, the correlation ratio, and other generalized metrics. Single slice/3D registration results on brain and heart volumes show that generalized metrics that deviate slightly from the Shannon metrics can improve registration outcomes based on success rate, and have competitive computation times. The results also suggest that these metrics may be used with Shannon (and other) measures in a complementary manner.

• Type: Conference
• Year: 2003
• Where: MICCAI
• Authors: Mark P. Wachowiak, Renata Smolíkova, Terry M. Peters
• Keywords: Information-theoretic Similarity Metrics, Medical Imaging, MICCAI 2003, Shannon Metrics, Shannon Mutual Information