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  • Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

    Author(s)
    Ng, SK
    McLachlan, GJ
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2004
    Metadata
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    Abstract
    Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing ...
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    Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain.
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    Journal Title
    Pattern Recognition
    Volume
    37
    Issue
    8
    DOI
    https://doi.org/10.1016/j.patcog.2004.02.012
    Subject
    Information systems
    Other information and computing sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/33488
    Collection
    • Journal articles

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