An automated method for gridding and clustering-based segmentation of cDNA microarray images.
Microarrays are widely used to quantify gene expression levels. Microarray image analysis is one of the tools, which are necessary when dealing with vast amounts of biological data. In this work we propose a new method for the automated analysis of microarray images. The proposed method consists of two stages: gridding and segmentation. Initially, the microarray images are preprocessed using template matching, and block and spot finding takes place. Then, the non-expressed spots are detected and a grid is fit on the image using a Voronoi diagram. In the segmentation stage, K-means and Fuzzy C means (FCM) clustering are employed. The proposed method was evaluated using images from the Stanford Microarray Database (SMD). The results that are presented in the segmentation stage show the efficiency of our Fuzzy C means-based work compared to the two already developed K-means-based methods. The proposed method can handle images with artefacts and it is fully automated.
Giannakeas
Nikolaos
N
Laboratory of Biological Chemistry, Medical School, University of Ioannina, Ioannina, Greece.
Fotiadis
Dimitrios I
DI
D016477:Artifacts; D016000:Cluster Analysis; D017143:Fuzzy Logic; D007089:Image Enhancement / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013050:Spectrometry, Fluorescence / Q000379:methods