Evaluation of brain MRI alignment with the robust hausdorff distance measures

Andriy Fedorov, Eric Billet, Marcel Prastawa, Guido Gerig, Alireza Radmanesh, Simon K. Warfield, Ron Kikinis, Nikos Chrisochoides

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    We present a novel automated method for assessment of image alignment, applied to non-rigid registration of brain Magnetic Resonance Imaging data (MRI) for image-guided neurosurgery. We propose a number of robust modifications to the Hausdorff distance (HD) metric, and apply it to the edges recovered from the brain MRI to evaluate the accuracy of image alignment. The evaluation results on synthetic images, simulated tumor growth MRI and real neurosurgery data with expert- identified anatomical landmarks, confirm that the accuracy of alignment error estimation is improved compared to the conventional HD. The proposed approach can be used to increase confidence in the registration results, assist in registration parameter selection, and provide local estimates and visual assessment of the registration error.

    Original languageEnglish (US)
    Title of host publicationAdvances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings
    Number of pages10
    EditionPART 1
    StatePublished - 2008
    Event4th International Symposium on Visual Computing, ISVC 2008 - Las Vegas, NV, United States
    Duration: Dec 1 2008Dec 3 2008

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 1
    Volume5358 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other4th International Symposium on Visual Computing, ISVC 2008
    Country/TerritoryUnited States
    CityLas Vegas, NV

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)


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