Effective quantification of gene expression levels in microarray images using a spot-adaptive compound clustering-enhancement-segmentation scheme

Abstract

A spot-adaptive compound clustering-enhancement-segmentation (CES) scheme was developed for the quantification of gene expression levels in microarray images. The CES-scheme employed 1/griding, for locating spotregions, 2/Fuzzy C-means clustering, for segmenting spots from background, 3/ background noise estimation and spot's center localization, 4/emphasizing of spot's outline by the CLAHE image enhancement technique, 5/segmentation by the SRG algorithm, using information from step 3, and 6/microarray spot intensity extraction. Extracted intensities by the CES-Scheme were compared against those obtained by the MAGIC TOOL's SRG. Kullback-Liebler metric's values for the CES-Scheme were on average double man MAGIC TOOL'S, with differences ranging from 1.45bits to 2.77bits in 7 cDNA images. Coefficient-of-Variation results showed significantly higher reproducibility (p<0.001) for the CES-Scheme in quantifying gene expression levels. Processing times for 1024x1024 16-bit microarray images containing 6400 spots were 300 and 487 seconds for the CES-Scheme and MAGIC TOOL respectively. © Springer-Verlag Berlin Heidelberg 2007.