A New Methodology for Noise Removal and Segmentation in Microarray Images

Abstract

Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Enhancement, Gridding, Segmentation and Intensity extraction are important steps in microarray image analysis. This paper presents a noise removal method in microarray images based on Bi-dimensional Variational Mode Decomposition (BVMD). VMD is a signal processing method which decomposes any input signal into discrete number of sub-signals (called Variational Mode Functions) with each mode chosen to be its band width in spectral domain. First the noisy image is processed using BVMD to produce BVMFs. Then Discrete Wavelet Transform (DWT) thresholding technique is applied to each BVMF for denoising. The denoised microarray image is reconstructed by the summation of BVMFs. The filtered image is segmented using fuzzy local information c- means clustering algorithm. This method is named as BVMD and DWT thresholding method. The proposed method is compared with DWT thresholding and BEMD and DWT thresholding methods. The qualitative and quantitative analysis shows that BVMD and DWT thresholding method produces better noise removal than other two methods and produces better segmentation quality.