Microarray Image Processing Based on Clustering and Morphological Analysis

Microarrays allow the monitoring of expressions for tens of thousands of genes simultaneously. Image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identification of differentially expressed genes. Image processing for microarray images includes three tasks: spot gridding, segmentation and information extraction. In this paper, we address the segmentation and information extraction problems, and proposed a new segmentation method based on K-means clustering and a new background and foreground correction algorithm based on mathematical morphological and histogram analysis for information extraction. The advantage of our method is that it does not have any restrictions for the shape of spots. We compare our experimental results with those obtained from the popular software GenePix.

• Type: Conference
• Year: 2003
• Where: APBC
• Authors: Shuanhu Wu, Hong Yan
• Keywords: APBC 2003, APBC 2007, Foreground Correction Algorithm, Information Extraction, Information Extraction Problems