Abstract
DNA microarray technology is extensively adopted in molecular biology experiments owing to its ability to monitor thousands of genes simultaneously on a single microarray image. However, computing spot features from a microarray image still relies on manual operations and adjustments, which is occasionally inefficient and not repeatable. Moreover, the details of microarray analysis by commercial software and bio-company are usually not revealed. The unclarity of analysis methods incurs the discredit of the experiment correctness. This thesis proposes an approach for measuring gene expression level nearly automatically and identifying potential genes for further clinical investigation. First, the cDNA microarray image is processed by graying, smoothing and rotating. Next, each spot of the image is located and split from background. The spot statistics are finally computed as features. Based on these features, the potential genes are identified according to their separation. Our approach is tested for computing spot features on 52 cDNA microarray images made from 26 pairs of normal and tumor tissues of patients of gastric cancer provided by the angiogenesis research center at National Taiwan University (ARCNTU) [13] and selects 15 genes for further clinical investigation.