• DocumentCode
    3182192
  • Title

    A quantitative analysis of F-actin features and distribution in fluorescence microscopy images to distinguish cells with different modes of motility

  • Author

    Jie Cheng ; Xiaoping Zhu ; Hao Cheng ; Hong Zhao ; Wong, Stephen T. C.

  • Author_Institution
    Syst. Med. & Bioeng. Dept., Weill Cornell Med. Coll., Houston, TX, USA
  • fYear
    2013
  • fDate
    3-7 July 2013
  • Firstpage
    136
  • Lastpage
    139
  • Abstract
    Actin is one of the most abundant proteins in eukaryote cells, playing a key role in cell dynamic morphological alterations and tumor metastatic spread. To investigate the relationship between the distribution patterns of actin and the aggressiveness of cancer cells, we developed an image analysis framework for quantifying cell F-actin distributions examined with fluorescence microscopy. The images are first segmented with multichannel information of both F-actin and nuclear staining. Using the watershed method and Voronoi tessellation, the cells can be well segmented based on both F-actin and nuclear information. Altogether, sixteen F-actin distribution features are calculated for each individual cell. A linear Support Vector Machine (SVM) is then applied in the feature space to separate cells with different modes of motility. Our results show that cells with different modes of motility can be distinguished by F-actin distributions. To our knowledge, this is the first study managing to distinguish cancer cells with different aggressiveness based on quantitative analysis of F-actin distribution patterns.
  • Keywords
    cancer; cell motility; fluorescence; image segmentation; medical image processing; molecular biophysics; optical microscopy; proteins; support vector machines; tumours; F-actin distribution feature; SVM; Voronoi tessellation; cancer cells; cell F-actin distributions; cell dynamic morphological alterations; cell motility; eukaryote cells; feature space; fluorescence microscopy image; image analysis framework; image segmentation; linear support vector machine; multichannel information; nuclear information; nuclear staining; proteins; quantitative analysis; tumor metastatic spread; watershed method; Biomedical imaging; Cancer; Feature extraction; Image segmentation; Microscopy; Nuclear measurements; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
  • Conference_Location
    Osaka
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2013.6609456
  • Filename
    6609456