title: An improved K-means clustering method for cDNA microarray image segmentation.
abstract: 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 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.
authors.affiliation: Innovation Center of Conceive Design Implement Operate, Xiamen Institute of Technology, Huaqiao University, Xiamen, China.
authors.forename: T N
authors.initials: TN
authors.lastname: Wang
authors.affiliation: Information Engineering College, Jimei University, Xiamen, China.
authors.forename: T J
authors.initials: TJ
authors.lastname: Li
authors.affiliation: Department of Automation, Xiamen University, Xiamen, China.
authors.forename: G F
authors.initials: GF
authors.lastname: Shao
authors.affiliation: Department of Automation, Xiamen University, Xiamen, China.
authors.forename: S X
authors.initials: SX
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*