Signal extraction from microarray images for gene array data analysis

Microarray technology enables simultaneous gene expression level monitoring for thousands of genes. While this technology has now been recognized as a powerful and cost-effective tool for large scale analysis, the many systematic sources of experimental variations introduce inherent errors in the extracted data. Data is gathered by processing scanned images of microarray slides. Therefore robust image processing is particularly important and has a large impact on downstream analysis. The processing of the scanned images subdivided into three phases: gridding, segmentation and data extraction, are dealt with in the paper. In this study, we have proposed a method of gridding based on spot spacing by autocorrelation of profile. The study also examined four segmentation methods: the global thresholding algorithm, the local thresholding algorithm, a combination of the two and pinhole filling algorithm. The results of algorithm are also compared. To measure the gene expression levels, the processing of cDNA microarray images must overcome a large set of issues in these three phases that motivates this study. These issues are studied and solutions also suggested.