A Microscopic image classification system for high-throughput cell-cycle screening

Tuan Pham, Dat Tran, Xiaobo Zhou, Steven Wong

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    Computerized high-throughput screening of cells using fluorescent microscopic imaging technology will tremendously help scientists gain the understanding of complex cellular processes that lead to drug discovery and disease treatment. Manual image analysis of cell images is very time-consuming, potentially inaccurate and poorly reproducible. Therefore the automation of cell-cycle screening, which has not been much explored, is critical for further biological downstream analysis. For such automation task, image classification of cell phases is considered to be most difficult. In this paper we present several computational models for the classification of cell nuclei in different mitotic phases recorded over a period of twenty-four hours at every fifteen minutes using time-lapse fluoresence microscopy. The experimental results have shown that the proposed methods are effective and can be useful for automating cell screening
    Original languageEnglish
    Pages (from-to)67-77
    Number of pages11
    JournalInternational Journal of Intelligent Computing in Medical Sciences & Image Processing
    Volume1
    Issue number1
    DOIs
    Publication statusPublished - 2007

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    Image classification
    Cell Cycle
    Screening
    Cells
    Throughput
    Automation
    Image analysis
    Microscopic examination
    Drug Discovery
    Cell Nucleus
    Imaging techniques
    Microscopy
    Technology

    Cite this

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    abstract = "Computerized high-throughput screening of cells using fluorescent microscopic imaging technology will tremendously help scientists gain the understanding of complex cellular processes that lead to drug discovery and disease treatment. Manual image analysis of cell images is very time-consuming, potentially inaccurate and poorly reproducible. Therefore the automation of cell-cycle screening, which has not been much explored, is critical for further biological downstream analysis. For such automation task, image classification of cell phases is considered to be most difficult. In this paper we present several computational models for the classification of cell nuclei in different mitotic phases recorded over a period of twenty-four hours at every fifteen minutes using time-lapse fluoresence microscopy. The experimental results have shown that the proposed methods are effective and can be useful for automating cell screening",
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    A Microscopic image classification system for high-throughput cell-cycle screening. / Pham, Tuan; Tran, Dat; Zhou, Xiaobo; Wong, Steven.

    In: International Journal of Intelligent Computing in Medical Sciences & Image Processing, Vol. 1, No. 1, 2007, p. 67-77.

    Research output: Contribution to journalArticle

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    AU - Pham, Tuan

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    AU - Zhou, Xiaobo

    AU - Wong, Steven

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