Calculating Covariances for Mutual Information Coregistration

MIUA 2004, London, U.K.

Mutual information (MI) has become a popular similarity measure in multi-modality medical image registration since it was first applied to the problem in 1995. This paper describes a method for calculating the covariance matrix for MI coregistration. We derive an expression for the covariance matrix by identifying MI as a biased log-likelihood measure. The validity of this result is then demonstrated through comparison with the results of Monte-Carlo simulations of the coregistration of T1-weighted to T2-weighted synthetic MRI scans of the brain. We conclude with some observations on the theoretical basis of MI as a log-likelihood.

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