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cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broad use is that it enables scientists to analyze simultaneously the expression levels of thousands of genes over different samples (Leung et al., 2003). More precisely, the process of a microarray experiment (Campbell et al., 2007) starts with the selection of a set of DNA probes that are of particular interest. A robot places the selected DNA probes on a glass slide, creating an invisible array of DNA dots. Two distinct populations of mRNAs (messenger RNAs) are then isolated from a control sample (i.e a cell developed under normal conditions) and a test sample (i.e. a cell developed under a specific treatment). The mRNA populations are reversely transcribed into cDNA (complementary DNA) populations which in turn are colored with separate fluorescent dyes of different wavelengths (i.e. Cy3 and Cy5). The dyed cDNA populations are mixed with purified water and the solution is placed on the glass slide in order for the cDNA populations to be hybridized with the slide’s DNA dots. Finally, the hybridized glass slide is fluorescently scanned twice; one scan for each dye’s wavelength. Hence, two digital images are produced, one for each population of mRNA. Each digital image contains a number of spots (corresponding to the DNA-cDNA dots) of various fluorescence intensities. Given that the intensity of each spot is proportional to the hybridization level of the cDNAs and the DNA dots, the gene expression information is obtained by analyzing the digital images. As stated by Yang et al (Yang et. al, 2002), the process of analyzing a microarray image can be divided into three main phases, namely: “Gridding”, “Spot-Segmentation” and “SpotIntensity extraction”. During the 1st phase, the microarray image is segmented into numerous compartments, each containing one individual spot and background. During the 2nd phase each compartment is individually segmented into a spot area and a background area, while during the 3rd phase the brightness of each spot is calculated. The expressionlevels of the genes in these spots are a direct result of their individual brightness. Amongst the stages of the microarray-image analysis, spot-segmentation remains the most challenging one. Ideally, the existing spots inside the microarray image are aligned in 2D array layouts. These ‘ideal spots’ also have a circular 2D shape with fixed diameters, while

A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images