Automated analysis of DNA hybridization images for high-throughput genomics

Authors:
Suchendra M. Bhandarkar;Tongzhang Jiang;Kunal Verma;Nan Li
Affiliations:
Department of Computer Science, 415 Boyd Graduate Studies Research Center, The University of Georgia, Athens, GA;Department of Computer Science, 415 Boyd Graduate Studies Research Center, The University of Georgia, Athens, GA;Department of Computer Science, 415 Boyd Graduate Studies Research Center, The University of Georgia, Athens, GA;Department of Computer Science, 415 Boyd Graduate Studies Research Center, The University of Georgia, Athens, GA
Venue:
Machine Vision and Applications
Year:
2004

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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.