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  • Segmentation of Brain MR Images with Bias Field Correction.

    Author(s)
    Kim, Seung-Gu
    Ng, Shu-Kay
    J. McLachlan, Geoffrey
    Wang, Deming
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2003
    Metadata
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    Abstract
    We consider a statistical model-based approach to the segmentation of magnetic resonance (MR) images with bias field correction. The proposed method of penalized maximum likelihood is implemented via the expectationconditional maximization (ECM) algorithm, using an approximation to the E-step based on a fractional weight version of the iterated conditional modes (ICM) algorithm. A Markov random field (MRF) is adopted to model the spatial dependence between neighouring voxels. The approach is illustrated using some simulated and real MR data.We consider a statistical model-based approach to the segmentation of magnetic resonance (MR) images with bias field correction. The proposed method of penalized maximum likelihood is implemented via the expectationconditional maximization (ECM) algorithm, using an approximation to the E-step based on a fractional weight version of the iterated conditional modes (ICM) algorithm. A Markov random field (MRF) is adopted to model the spatial dependence between neighouring voxels. The approach is illustrated using some simulated and real MR data.
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    Conference Title
    Proceedings of the 2003 APRS Workshop on Digital Image Computing (WDIC 2003)
    Publisher URI
    http://www.aprs.org.au/wdic2003/CDROM/index.html
    Subject
    Multi-Disciplinary
    Publication URI
    http://hdl.handle.net/10072/32145
    Collection
    • Conference outputs

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