Fuzzy Information Fusion of Classification Models for High-Throughput Image Screening of Cancer cells in Time-Lapse Microscopy

Tuan Pham, Dat Tran, Xiaobo Zhou

    Research output: Contribution to journalArticle

    Abstract

    Bioimaging at molecular and cellular levels requires specific image analysis methods to help life scientists develop methodologies and hypotheses in biology and biomedicine. In particular, this is true when dealing with microscopic images of cells and vessels. To facilitate the automation of cell screening, we have developed methods based on vector quantization and Markov model for classification of cellular phases using time-lapse fluorescence microscopic image sequences. Because of ambiguity inherently existing in the labeling of cell-phase feature vectors, we proposed to use relaxation labeling technique to reduce uncertainty among cell-phase models having overlapping properties. To further improve the classification rate we applied a fuzzy fusion strategy for combining individual results obtained from multiple classifiers. Our proposed image-classification methods can be useful for the task of high-content cell-cycle screening which is essential for biomedical research in the study of structures and functions of cells and molecules
    Original languageEnglish
    Pages (from-to)237-246
    Number of pages10
    JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
    Volume11
    Issue number4
    DOIs
    Publication statusPublished - 2007

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    Information fusion
    Labeling
    Microscopic examination
    Screening
    Cells
    Throughput
    Image classification
    Vector quantization
    Image analysis
    Classifiers
    Automation
    Fluorescence
    Molecules
    Uncertainty

    Cite this

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    title = "Fuzzy Information Fusion of Classification Models for High-Throughput Image Screening of Cancer cells in Time-Lapse Microscopy",
    abstract = "Bioimaging at molecular and cellular levels requires specific image analysis methods to help life scientists develop methodologies and hypotheses in biology and biomedicine. In particular, this is true when dealing with microscopic images of cells and vessels. To facilitate the automation of cell screening, we have developed methods based on vector quantization and Markov model for classification of cellular phases using time-lapse fluorescence microscopic image sequences. Because of ambiguity inherently existing in the labeling of cell-phase feature vectors, we proposed to use relaxation labeling technique to reduce uncertainty among cell-phase models having overlapping properties. To further improve the classification rate we applied a fuzzy fusion strategy for combining individual results obtained from multiple classifiers. Our proposed image-classification methods can be useful for the task of high-content cell-cycle screening which is essential for biomedical research in the study of structures and functions of cells and molecules",
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    AU - Tran, Dat

    AU - Zhou, Xiaobo

    PY - 2007

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    N2 - Bioimaging at molecular and cellular levels requires specific image analysis methods to help life scientists develop methodologies and hypotheses in biology and biomedicine. In particular, this is true when dealing with microscopic images of cells and vessels. To facilitate the automation of cell screening, we have developed methods based on vector quantization and Markov model for classification of cellular phases using time-lapse fluorescence microscopic image sequences. Because of ambiguity inherently existing in the labeling of cell-phase feature vectors, we proposed to use relaxation labeling technique to reduce uncertainty among cell-phase models having overlapping properties. To further improve the classification rate we applied a fuzzy fusion strategy for combining individual results obtained from multiple classifiers. Our proposed image-classification methods can be useful for the task of high-content cell-cycle screening which is essential for biomedical research in the study of structures and functions of cells and molecules

    AB - Bioimaging at molecular and cellular levels requires specific image analysis methods to help life scientists develop methodologies and hypotheses in biology and biomedicine. In particular, this is true when dealing with microscopic images of cells and vessels. To facilitate the automation of cell screening, we have developed methods based on vector quantization and Markov model for classification of cellular phases using time-lapse fluorescence microscopic image sequences. Because of ambiguity inherently existing in the labeling of cell-phase feature vectors, we proposed to use relaxation labeling technique to reduce uncertainty among cell-phase models having overlapping properties. To further improve the classification rate we applied a fuzzy fusion strategy for combining individual results obtained from multiple classifiers. Our proposed image-classification methods can be useful for the task of high-content cell-cycle screening which is essential for biomedical research in the study of structures and functions of cells and molecules

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    EP - 246

    JO - International Journal of Knowledge-Based and Intelligent Engineering Systems

    JF - International Journal of Knowledge-Based and Intelligent Engineering Systems

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