K-Means with Bi-dimensional Empirical Mode Decomposition for segmentation of microarray image

A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. Clustering algorithms have been applied for segmenting the microarray image. Among these approaches, K-Means method is simple one. However, microarray image contains noise and the noise would affect the segmentation results. In this paper, we propose to combine the K-means method with Bi-dimensional Empirical Mode Decomposition for segmenting the microarray image in order to reduce the effect of noise. We call this method as K-Means with Bi-dimensional Empirical Mode decomposition (KBEMD). Using the KBEMD method on microarray image, we obtain better results than those using K-Means only. Using the KBEMD method to analyze microarray image can save time and obtain more reasonable results.