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  • Robust Adaptive Spot Segmentation of DNA Microarray Images

    Author(s)
    Liew, AWC
    Yan, H
    Yang, MS
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2003
    Metadata
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    Abstract
    The rapid advancement ofDNA chip (microarray) technology has revolutionalized genetic research in bioscience. However, the enormous amount ofdata produced from a microarray image makes automatic computer analysis indispensable. An important 3rst step in analyzing microarray image is the accurate determination ofthe DNA spots in the image. We report here a novel spot segmentation method for DNA microarray images. The algorithm makes use of adaptive thresholding and statistical intensity modeling to: (i) generate the grid structure automatically, where each subregion in the grid contains only one spot, and (ii) to segment the ...
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    The rapid advancement ofDNA chip (microarray) technology has revolutionalized genetic research in bioscience. However, the enormous amount ofdata produced from a microarray image makes automatic computer analysis indispensable. An important 3rst step in analyzing microarray image is the accurate determination ofthe DNA spots in the image. We report here a novel spot segmentation method for DNA microarray images. The algorithm makes use of adaptive thresholding and statistical intensity modeling to: (i) generate the grid structure automatically, where each subregion in the grid contains only one spot, and (ii) to segment the spot, ifany, within each subregion. The algorithm is fully automatic, robust, and can aid in the high throughput computer analysis ofmicroarray data.
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    Journal Title
    Pattern Recognition
    Volume
    36
    Issue
    5
    Publisher URI
    http://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#description
    DOI
    https://doi.org/10.1016/S0031-3203(02)00170-X
    Subject
    Artificial Intelligence and Image Processing
    Information Systems
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/15508
    Collection
    • Journal articles

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