Microarray image segmentation using clustering methods

Abstract

Microarray image processing is a technology for viewing and computationally measuring thousands of genes at the same time. Gene expressions provide information about the cell activity in an organism. Observing a substantial change in gene expressions between the cDNA (complementary DNA) microarray experiments of an organism can be a sign of a disease. The goal of this study is to make a fine distinction against the gene expressions in the microarray image processing. For this reason, two clustering methods have been experimented and compared. In this study we have specifically investigated the segmentation step of the microarray image. Other than the segmentation methods used in commercial packages we have used the clustering techniques. We have applied fuzzy c-means and k-means methods and observed the results. © Association for Scientific Research.