Computing Covariances for Mutual Information Coregistration

Paul Bromiley, Daniel Rueckert (Editor), Jo Hajnal (Editor), Guang-Zhong Yang (Editor)

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    Mutual Information (MI) has become a popular similarity measure in multi-modal 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 MR scans of the brain. We conclude with some observations on the theoretical basis of MI as a log-likelihood.
    Original languageEnglish
    Title of host publicationProc. 8th Medical Image Understanding and Analysis Conference
    EditorsDaniel Rueckert, Jo Hajnal, Guang-Zhong Yang
    Place of PublicationImperial College London
    PublisherBMVA Press
    Number of pages4
    Publication statusPublished - 2004
    EventMedical Image Understanding and Analysis 2004 - Imperial College London
    Duration: 23 Sept 200424 Sept 2004


    ConferenceMedical Image Understanding and Analysis 2004
    CityImperial College London


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