Tracking virus particles in fluorescence microscopy images using multi-scale detection and multi-frame association

Astha Jaiswal, William J. Godinez, Roland Eils, Maik Jorg Lehmann, Karl Rohr

    Research output: Contribution to journalArticlepeer-review

    17 Scopus citations

    Abstract

    Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

    Original languageEnglish (US)
    Pages (from-to)4122-4136
    Number of pages15
    JournalIEEE Transactions on Image Processing
    Volume24
    Issue number11
    DOIs
    StatePublished - Nov 1 2015

    Keywords

    • Virus particle tracking
    • multi-frame association
    • multi-scale particle detection

    ASJC Scopus subject areas

    • Software
    • Computer Graphics and Computer-Aided Design

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