Genes expression level quantification using a spot-based algorithmic pipeline.
An efficient spot-based (SB) algorithmic pipeline of clustering, enhancement, and segmentation techniques was developed to quantify gene expression levels in microarray images. The SB-pipeline employed i/a griding procedure to locate spot-regions, ii/a clustering algorithm (enhanced fuzzy c-means or EnFCM) to roughly segment spots from background and estimate background noise and spot's center, iii/an adaptive histogram modification technique to accentuate spot's boundaries, and iv/a segmentation algorithm (Seeded Region Growing or SRG), to extract microarray spots' intensities. Extracted intensities were comparatively evaluated in term of Mean Absolute Error (MAE) against the MAGIC TOOL's SRG employing a dataset of 7 replicated microarray images (6400 spots each). MAE box-plots mean values were 0.254 and 0.630 for the SB-pipeline and the MAGIC TOOL respectively. Total processing times for the dataset evaluated (7 images) were 2100 seconds and 3410 seconds for the SB-pipeline and MAGIC TOOL respectively.
Daskalakis
Antonis
A
Medical Image Processing and Analysis Group, Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26503, Greece. daskalakis@med.upatras.gr
Cavouras
Dionisis
D
Bougioukos
Panagiotis
P
Kostopoulos
Spiros
S
Georgiadis
Pantelis
P
Kalatzis
Ioannis
I
Kagadis
George
G
Nikiforidis
George
G
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity