Mutual information 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 mutual information coregistration. We derive an expression for the matrix through identification of mutual information with a log-likelihood measure. The validity of this result is then demonstrated through comparison with the results of Monte-Carlo simulations of the coregistration of Tl-weighted to T2-weighted synthetic and genuine MRI scans of the brain. We conclude with some observations on the theoretical basis of the mutual information measure as a log-likelihood. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Bromiley, P. A., Pokric, M., & Thacker, N. A. (2004). Empirical evaluation of covariance estimates for mutual information coregistration. In Lecture Notes in Computer Science (Vol. 3216, pp. 607–614). Springer Verlag. https://doi.org/10.1007/978-3-540-30135-6_74
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