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Reports Year : 1999

Unifying Maximum Likelihood Approaches in Medical Image Registration

Grégoire Malandain
Nicholas Ayache


While intensity-based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions 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 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 correspondin- g 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 modalities of images to illustrate the importance of choosing an appropriate similarity measure.


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Dates and versions

inria-00072923 , version 1 (24-05-2006)


  • HAL Id : inria-00072923 , version 1


Alexis Roche, Grégoire Malandain, Nicholas Ayache. Unifying Maximum Likelihood Approaches in Medical Image Registration. RR-3741, INRIA. 1999. ⟨inria-00072923⟩
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