International Journal of Computer Science Issues

More than a traditional journal...

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, Fuzzy C-means (FCM) method is simple and efficient one. However, microarray image contains noise and the noise would affect the segmentation results. In this paper, we propose to combine the FCM 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 Fuzzy C-means with Bi-dimensional Empirical Mode decomposition (FCMBEMD). We use an adaptive local weighted averaging filter in the BEMD method for removing the noise in the image and finally K-means algorithm is used for segmentation of image. Using the FCMBEMD method on microarray image, we obtain better results than those using FCM only. Using the FCMBEMD method to analyze microarray image can save time and obtain more reasonable results.