An improved K-means clustering method for cDNA microarray image segmentation

T.N. Wang, T.J. Li, G.F. Shao, S.X. Wu
2015 Genetics and Molecular Research  
Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two
more » ... the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.
doi:10.4238/2015.july.14.3 pmid:26214458 fatcat:uuvcs3socnfndav3rzjxeaiije