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 spot-regions, 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 than 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.
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Daskalakis, A. et al. (2007). Effective Quantification of Gene Expression Levels in Microarray Images Using a Spot-Adaptive Compound Clustering-Enhancement-Segmentation Scheme. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_48
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DOI: https://doi.org/10.1007/978-3-540-74484-9_48
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