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CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

A detection algorithm based on matrix factorization for live mitochondria in fluorescent microscopic images

  • School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.10.007
  • Received Date: 18 March 2014
  • Accepted Date: 16 May 2014
  • Rev Recd Date: 16 May 2014
  • Publish Date: 30 October 2014
    Abstract

  • Abstract

    Detection of mitochondria in fluorescent microscopic images is one of the most important methods in studies concerning apoptosis and the nature of life phenomena in the area of biomedical image processing. Limited by fluorescence microscopy, fluorescent microscopic images contain two parts which are the shadow of cytoplasm and live mitochondria, and the signal-to-noise ratio (SNR) of live mitochondria time sequence images is low, which cannot meet the requirements of general particle algorithm. A new detection algorithm was proposed for live mitochondria in fluorescent microscopic images. To realize this method rapidly, augmented Lagrange multiplier algorithm was used. Mitochondria was be separated from the cytoplasm and accurately detected in fluorescent microscopic images. Therefore, the proposed algorithm provides an efficient and accurate tool to detect mitochondria in live cell.

    Abstract

    Detection of mitochondria in fluorescent microscopic images is one of the most important methods in studies concerning apoptosis and the nature of life phenomena in the area of biomedical image processing. Limited by fluorescence microscopy, fluorescent microscopic images contain two parts which are the shadow of cytoplasm and live mitochondria, and the signal-to-noise ratio (SNR) of live mitochondria time sequence images is low, which cannot meet the requirements of general particle algorithm. A new detection algorithm was proposed for live mitochondria in fluorescent microscopic images. To realize this method rapidly, augmented Lagrange multiplier algorithm was used. Mitochondria was be separated from the cytoplasm and accurately detected in fluorescent microscopic images. Therefore, the proposed algorithm provides an efficient and accurate tool to detect mitochondria in live cell.
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    • References(9)
  • [1]
    Conradt B. Cell biology: Mitochondria shape up[J]. Nature, 2006, 443(7112): 646-647.
    [2]
    Xu J M, Li Y, Du S D, et al. A tracking algorithm for live mitochondria in fluorescent microscopy images[J]. Journal of Biomedical Engineering, 2012, 29(2): 352-358.
    徐俊梅, 李杨, 都思丹, 等. 荧光显微图像序列中活性线粒体的跟踪算法[J]. 生物医学工程学杂志, 2012, 29(2): 352-358.
    [3]
    Olivo-Marin J C. Extraction of spots in biological images using multi-scale products[J]. Pattern Recognition, 2002, 35(9): 1 989-1 996.
    [4]
    Jiang S, Zhou X B, Kirchhausen T, et al. Detection of molecular particles in live cells via machine learning[J]. Cytometry A, 2007, 71(8): 563-575.
    [5]
    Baraniuk R. Compressive sensing[J]. IEEE signal processing magazine, 2007, 24(4): 118-121.
    [6]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1 289-1 306.
    [7]
    Pilling A D, Horiuchi D, Lively C M, et al. Kinesin-1 and Dynein are the primary motors for fast transport of mitochondria in Drosophila motor axons[J]. Molecular Biology of the Cell, 2006, 17(4): 2 057-2 068.
    [8]
    Candès E J, Li X D, Ma Y, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3): 1-39.
    [9]
    Lin Z C, Chen M M, Ma Y. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[EB/OL]. http://www.researchgate.net/publication/46587364_The_Augmented_Lagrange_Multiplier_Method_for_Exact_Recovery_of_Corrupted_Low-Rank_Matrices.
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    [1]
    Conradt B. Cell biology: Mitochondria shape up[J]. Nature, 2006, 443(7112): 646-647.
    [2]
    Xu J M, Li Y, Du S D, et al. A tracking algorithm for live mitochondria in fluorescent microscopy images[J]. Journal of Biomedical Engineering, 2012, 29(2): 352-358.
    徐俊梅, 李杨, 都思丹, 等. 荧光显微图像序列中活性线粒体的跟踪算法[J]. 生物医学工程学杂志, 2012, 29(2): 352-358.
    [3]
    Olivo-Marin J C. Extraction of spots in biological images using multi-scale products[J]. Pattern Recognition, 2002, 35(9): 1 989-1 996.
    [4]
    Jiang S, Zhou X B, Kirchhausen T, et al. Detection of molecular particles in live cells via machine learning[J]. Cytometry A, 2007, 71(8): 563-575.
    [5]
    Baraniuk R. Compressive sensing[J]. IEEE signal processing magazine, 2007, 24(4): 118-121.
    [6]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1 289-1 306.
    [7]
    Pilling A D, Horiuchi D, Lively C M, et al. Kinesin-1 and Dynein are the primary motors for fast transport of mitochondria in Drosophila motor axons[J]. Molecular Biology of the Cell, 2006, 17(4): 2 057-2 068.
    [8]
    Candès E J, Li X D, Ma Y, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3): 1-39.
    [9]
    Lin Z C, Chen M M, Ma Y. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[EB/OL]. http://www.researchgate.net/publication/46587364_The_Augmented_Lagrange_Multiplier_Method_for_Exact_Recovery_of_Corrupted_Low-Rank_Matrices.
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