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. A recursive segmentation procedure is designed and implemented to extract spot-like features in the hybridization images in the presence of a highly inhomogeneous background. Positive hybridization signals (hits) are extracted from the spot-like features using grouping and decomposition algorithms based on computational geometry. A mathematical model for the positive hybridization patterns and a pattern classifier based on shape-based moments are proposed and implemented to distinguish between the clone-probe hybridization signals.

MOTIVATION: Contig maps are a type of physical map that show the native order of a set of overlapping genomic clones. Overlaps between clones can be detected by finding common sequences using a number of experimental protocols including hybridization of probes. All current mapping algorithms of which we are aware require that hybridizations be scored using a fixed number of discrete values (typically 0/1 or high/medium/low). When hybridization data is captured automatically using digital equipment, this provides the opportunity for hybridization intensities to be used in map construction. More fine-grained distinctions in the levels of hybridization may be exploited by algorithms to generate more accurate physical maps. RESULTS: We describe an approach to creating contig maps that uses measured hybridization intensities instead of data scored with a fixed number of discrete values. We describe and compare four algorithms for creating physical maps with hybridization intensities. Simulations using measured intensities sampled from actual data on Aspergillus nidulans indicate that using hybridization intensities rather than data that is automatically scored with respect to threshold values may yield more accurate physical maps.

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 is imperative for high-throughput genomics experiments. A recursive segmentation procedure is designed and implemented to extract spot-like features in the hybridization images in the presence of a highly inhomogeneous background. Positive hybridization signals (hits) are extracted from the spot-like features using grouping and decomposition algorithms based on computational geometry. A mathematical model for the positive hybridization patterns and a pattern classifier based on shape-based moments are proposed and implemented to distinguish between the clone-probe hybridization signals. Experimental results on real DNA hybridization images are presented.