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While intensity-based similarity measures are increasingly used for medical image registration, they often rely on implicit assump- tions regarding the physics of imaging. The motivation of this paper is to determine what are the assumptions corresponding to a number of popular similarity measures in order to better understand their use, and finally help choosing the one which is the most appropriate for a given class of problems. After formalizing registration based on general im- age acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then de- rive similarity measures corresponding to dierent modeling assumptions and retrieve some well-known measures (correlation coecient, correla- tion ratio, mutual information). Finally, we present results of registration between 3D MR and 3D Ultrasound images to illustrate the importance of choosing an appropriate similarity measure.

Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration