Empirical Evaluation of Covariance Estimates for Mutual Information Coregistration

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 T1-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.

• Type: Conference
• Year: 2004
• Where: MICCAI
• Authors: Paul A. Bromiley, Maja Pokric, Neil A. Thacker
• Keywords: Medical Imaging, MICCAI 2004, Mutual Information, Mutual Information Coregistration, Popular Similarity Measure