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    A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences.

    Access Status
    Fulltext not available
    Authors
    Rezatofighi, S.
    Gould, S.
    Vo, Ba-Ngu
    Mele, K.
    Hughes, W.
    Hartley, R.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Rezatofighi, S. and Gould, S. and Vo, B. and Mele, K. and Hughes, W. and Hartley, R. 2013. A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences.. Information processing in medical imaging : proceedings of the ... conference. 23: pp. 110-122.
    Source Title
    Information processing in medical imaging : proceedings of the ... conference
    ISSN
    1011-2499
    School
    School of Electrical Engineering and Computing
    URI
    http://hdl.handle.net/20.500.11937/56341
    Collection
    • Curtin Research Publications
    Abstract

    Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.

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