Classification of Cell Phases in Time-Lapse by Vector Quantization and Markov Models

T Pham, Dat Tran, Xiaobo Zhou, T Wong

    Research output: A Conference proceeding or a Chapter in BookChapter

    Abstract

    Advances in fluorescent probing and microscopic imaging technology provide important tools for biology and medicine research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and
    tracking of individual cells in a dynamic cellular population. Image classification of cell phases in a fully automatic manner presents the most difficult task of such analysis. We are interested in applying several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested several proposed computational procedures with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and has potential for higher performance with better cellular feature extraction strategy
    Original languageEnglish
    Title of host publicationNeural Stem Cell Research
    EditorsEric V Grier
    Place of PublicationNew York
    PublisherNova Science Publishers Inc
    Chapter7
    Pages155-174
    Number of pages20
    ISBN (Print)9781594548468
    Publication statusPublished - 2006

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  • Cite this

    Pham, T., Tran, D., Zhou, X., & Wong, T. (2006). Classification of Cell Phases in Time-Lapse by Vector Quantization and Markov Models. In E. V. Grier (Ed.), Neural Stem Cell Research (pp. 155-174). Nova Science Publishers Inc.