A Fully Automated Gridding Technique for Real Composite cDNA Microarray Images

Abstract

Genome-wide screening using microarrays of DNA will be of great use in the early diagnosis of diseases such as cancer and HIV. It also makes use of gene discovery, pharmacogenomics, toxicogenomics, and nutrigenomics for other applications. A DNA microarray image lays out an orderly arranged specific gene regions called spots. Microarray image analysis consists primarily of preprocessing, spot area gridding, spot segmentation, and intensity extraction. The first two phases are focused on this work: preprocessing and gridding. The experiment is conducted on real composite cDNA microarray images. A composite microarray image is formed by suitably stacking a red channel image and a green channel image acquired from a microarray experiment either in the RGB domain or in the GRB domain. The blue channel is kept as zero. In order to reduce the challenging problems of microarray images, an efficient preprocessing algorithm is proposed here for these composite images. We have developed a fully automated gridding algorithm integrating global subgrid gridding and local gridding of spots. This technique extracts the structural information namely inter-subgrid spacing, inter-spot spacing and spot center position to achieve efficient gridding. The traits of a microarray image are evaluated using three parameters namely Mean square error, Naturalness quality image evaluator and degree of contrast. The accuracy of the experimental results indicates that this combined preprocessing and gridding technique performs better than existing competitive methods in SIB, GEO, SMD and DeRisi datasets which are most commonly used by the research community for microarray image analysis techniques.

Keywords

• cDNA
• composite microarray images
• contrast enhancement
• denoising
• fully automated gridding
• genome-wide monitoring