A Novel Hybrid Fuzzy Clustering based approach for the effective Quantification and Analysis of cDNA Microarray Images

In this paper, we propose a hybrid approach for microarray image analysis, which is to quantify the intensity of each spot and locate differentially articulated genes with the aid of image processing and machine learning techniques. Initially we employ a hill-climbing automatic gridding and spot quantification technique, which takes a microarray image (or a sub-grid) as input, and makes no assumptions concerning the size of the spots, rows and columns in the grid. We propose an approach based on image processing techniques for microarray image segmentation that includes a noise-removal pre-processing stage. The foreground and background pixels from the microarray images are segmented with the aid of morphological operator and common subtraction procedure whereas the noise is filtered by using wiener filtering. Finally for cluster analysis we employed a hybrid approach based on clustering techniques; Fuzzy C Means and Fuzzy K Means. Clustering and their analysis were performed on this inputted microarray data. To quantify the effectiveness of the proposed approach, we utilized the Microarray database which is available publicly and we evaluated the accuracy, the specificity and the sensitivity of our proposed approach. Index Terms — Bioinformatics, DNA Microarray Gene Expression, Gridding, Hill Climbing, Image Segmentation, Morphological Operators, Hybrid clustering. Microarray Analysis Normalization, Spot Localization, Wiener Filter, ——————————  ——————————