A classification-based segmentation of cDNA microarray images using Support Vector Machines.
Microarray technology provides a tool for the simultaneous analysis of the expression level for an amount of genes. Microarray studies have been shown that image processing techniques can significantly influence microarray data precision. In this paper we propose a supervised method for the segmentation of microarray images based on classification techniques. Support Vector machine is employed to classify each pixel of the image into signal, background or artefacts. In addition, a preprocessing step is applied in order to filter the initial image. The proposed method is applied both to real and simulated images. The pixels of the image are classified in two classes for the real images and three classes for the simulated one. For this task, an informative set of features is used from both green and red channels. The results obtained indicate high accuracy (approximately 99%).
Giannakeas
Nikolaos
N
Laboratory of Biological Chemistry, Medical School, University of Ioannina, Ioannina GR 45110, Greece. me01310@cc.uoi.gr
Karvelis
Petros S
PS
Fotiadis
Dimitrios I
DI
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / 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