Two fully automatic restoration-segmentation algorithms are proposed for the processing of biased magnetic resonance images. A first approach is based on an expectation-maximization procedure, where the initial conditions for the class distribution parameters and the number of classes are obtained, without any a priori knowledge, from a mode-based analysis of the biased image. A second approach relies completely on the mode-based analysis to update the number of classes and distribution parameters in every iteration. Both methods give accurate results even for overlapping distributions distorted by a gain factor of up to 40%. The possibility of having automatic initial conditions provides an important enhancement to previously reported methods.


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    Title :

    Automatic correction of bias field in magnetic resonance images


    Contributors:
    Garza-Jinich, M. (author) / Yanez, O. (author) / Medina, V. (author) / Meer, P. (author)


    Publication date :

    1999-01-01


    Size :

    123482 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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