Improving Microarray Spots Segmentation by K-Means driven Adaptive Image Restoration

Complementary DNA microarray experiments are used to study human genome. However, microarray images are corrupted by spatially inhomogeneous noise that deteriorates image and consequently gene expression. An adaptive microarray image restoration technique is developed by suitably combining unsupervised clustering with the restoration filters for boosting the performance of microarray spots segmentation and for improving the accuracy of subsequent gene expression. Microarray images comprised a publicly available dataset of seven images, obtained from the database of the MicroArray Genome Imaging & Clustering Tool website. Each image contained 6400 spots investigating the diauxic shift of Saccharomyces cerevisiae. The adaptive microarray image restoration technique combined 1/a griding algorithm for locating individual cell images, 2/a clustering algorithm, for assessing local noise from the spot’s background, and 3/a wiener restoration filter, for enhancing individual spots. The effect of the proposed technique quantified using a well-known boundary detection algorithm (Gradient Vector Flow snake) and the information theoretic metric of Jeffrey’s divergence. The proposed technique increased the Jeffrey’s metric from 0.0194 bits to 0.0314 bits, while boosted the performance of the employed boundary detection algorithm. Application of the proposed technique on cDNA microarray images resulted in noise suppression and facilitated spot edge detection.