Segmentation of Microarray Image Using Information Bottleneck
DNA microarrays provide a simple tool to identify andquantify the gene expression for tens of thousands of genessimultaneously. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction.Spots segmentation, which isto distinguish the spot signals from background pixels,is a critical step in microarray image processing. In this paper, new image segmentation algorithm based on the hard version of the information bottleneck method is presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. The input variable here, is the histogram bins and the output variable is the set of regions obtained from the split and merge algorithm. The proposed method is compared with existing segmentation methods such as k-means and Fuzzy C-means. The experimental results show that the proposed algorithm has segmented spots of the microarray image more accurately than other segmentation methods.
