Automatic Segmentation of cDNA Microarray Images Using Different Methods

Abstract

Due to the vast success of bioengineering techniques, a series of large scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. The analysis of DNA microarray image consists of several steps; gridding, segmentation, and quantification that can significantly deteriorate the quality of gene expression in formation, and hence decrease our confidence in any derived research results. Thus, microarray data processing steps become critical for performing optimal microarray data analysis and deriving meaningful biological information from microarray images .Segmentation is the process, by which each individual cell in the grid must be selected to determine the spot signal and to estimate the background hybridization. In this paper, four segmentation methods are explored; “fixed circle”, “adaptive circle”, “thresholding”, and “adaptive shape” segmentation. By comparing the results, it was found that the “adaptive shape segmentation method” can segment noisy microarray images correctly, gives high accuracy results and minimal processing time, and can be applied to various types of noisy microarray images.