Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate

Christian Brechbühler, Guido Gerig, Gábor Székely

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

    Abstract

    A novel bias correction technique is proposed based on the estimation of the parameters of a polynomial bias field directly from image data. The procedure overcomes difficulties known from homomor-phic filtering or from techniques assuming an initial presegmented image. The only parameters are a set of expected class means and the standard deviation. Applications to various MR images illustrate the performance.

    Original languageEnglish (US)
    Title of host publicationVisualization in Biomedical Computing - 4th International Conference, VBC 1996, Proceedings
    EditorsKarl Heinz Hohne, Ron Kikinis
    PublisherSpringer Verlag
    Pages141-146
    Number of pages6
    ISBN (Print)3540616497, 9783540616498
    DOIs
    StatePublished - 1996
    Event4th International Conference on Visualization in Biomedical Computing, VBC 1996 - Hamburg, Germany
    Duration: Sep 22 1996Sep 25 1996

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume1131
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other4th International Conference on Visualization in Biomedical Computing, VBC 1996
    CountryGermany
    CityHamburg
    Period9/22/969/25/96

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Brechbühler, C., Gerig, G., & Székely, G. (1996). Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate. In K. H. Hohne, & R. Kikinis (Eds.), Visualization in Biomedical Computing - 4th International Conference, VBC 1996, Proceedings (pp. 141-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1131). Springer Verlag. https://doi.org/10.1007/bfb0046948