Variational Mode Decomposition and Multiple Feature Segmentation on 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 Variational Mode Decomposition (VMD). 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 processedusing2-MDDVto produce 2-        DVMFs. Then Discrete Wavelet Transform (DWT) thresholding technique is applied to each VMF for denoising. The denoised microarray image is reconstructed by the summation of DWT filtered VMFs and there constructed image is segmented using FCM Clustering  method with each pixel having multiple features. This filtering mechanism is named as 2-D VMD-DWT thresholding method and segmentation mechanism is named as multiple feature FCM method. The proposed filtering method is compared BEMD-DWT thresholding method. The qualitative and quantitative analysis shows that 2-D VMD-DWT thresholding method produces better noise removal than BEMD-DWT thresholding method and produces better segmentation results with multiple features for each pixel than a single feature.