Unifying maximum likelihood approaches in medical image registration

Although intensity-based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the imaging physics. This paper clarifies the assumptions on which a number of popular similarity measures rely. After formalizing registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to different modeling assumptions and retrieve some well-known measures (correlation coefficient, correlation ratio, mutual information). Finally, we present results of rigid registration between several image modalities to illustrate the importance of choosing an appropriate similarity measure.