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Historically, microarray image processing has been technically challenging in obtaining quality gene expression data. After hybridization of Cy3- and Cy5-labeled samples, images are collected and processed to obtain gene expression ratio measurements for each of the elements on the array. The hybridization process often brings in contaminating noise, which can make correct identification of the signal difficult. In addition, spot intensity levels are highly variable due to the expression differences of different genes, and weak spots are often difficult to detect. These conditions are further complicated by inherent irregularities in spot position, shape, and size commonly found on high-density microarrays, making image processing an often labor-intensive task that is difficult to reliably automate. We previously reported a novel third-dye array visualization (TDAV) technology that allows prehybridization visualization and quality control of printed arrays. Here, we present a new microarray image processing approach utilizing TDAV. By incorporating the third-dye image, we show that overall quality of the microarray data is significantly improved, and automation of processing is feasible and reliable. Furthermore, we demonstrate use of the third-dye image to better quality control microarray image analysis. Both the principle and implementation of the approach are presented in detail, with experimental results.