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Automated analysis of DNA hybridization images for high-throughput genomics

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Abstract.

The design and implementation of a computer vision system called DNAScan for the automated analysis of DNA hybridization images is presented. The hybridization of a DNA clone with a radioactively tagged probe manifests itself as a spot on the hybridization membrane. The imaging of the hybridization membranes and the automated analysis of the resulting images are imperative for high-throughput genomics experiments. A recursive segmentation procedure is designed and implemented to extract spotlike features in the hybridization images in the presence of a highly inhomogeneous background. Positive hybridization signals (hits) are extracted from the spotlike features using grouping and decomposition algorithms based on computational geometry. A mathematical model for the positive hybridization patterns and a Bayesian pattern classifier based on shape-based moments are proposed and implemented to distinguish between the clone-probe hybridization signals. Experimental results on real hybridization membrane images are presented.

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Authors and Affiliations

  1. Department of Computer Science, 415 Boyd Graduate Studies Research Center, The University of Georgia, GA 30602-7404, Athens, USA

    Suchendra M. Bhandarkar, Tongzhang Jiang, Kunal Verma & Nan Li

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  1. Suchendra M. Bhandarkar
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  2. Tongzhang Jiang
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  3. Kunal Verma
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  4. Nan Li
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Correspondence to Suchendra M. Bhandarkar.

Additional information

Received: 25 June 2002, Accepted: 11 November 2003, Published online: 17 February 2004

Correspondence to: Suchendra M. Bhandarkar

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Bhandarkar, S.M., Jiang, T., Verma, K. et al. Automated analysis of DNA hybridization images for high-throughput genomics. Machine Vision and Applications 15, 121–138 (2004). https://doi.org/10.1007/s00138-003-0134-1

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