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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 12    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 12, December 2011
A Novel Hybrid Fuzzy Clustering based approach for the effective Quantification and Analysis of cDNA Microarray Images
Full Text(PDF, 3000)  PP.  
Author(s)
A.Sri Nagesh, Dr.G.P.Saradhi Varma, Dr A Govardhan
KEYWORDS
Bioinformatics, DNA Microarray Gene Expression, Gridding, Hill Climbing, Image Segmentation, Morphological Operators, Hybrid clustering. Microarray Analysis Normalization, Spot Localization, Wiener Filter,
ABSTRACT
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 au-tomatic gridding and spot quantification technique, which takes a microarray image (or a sub-grid) as input, and makes no assumptions con-cerning 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 fil-tering. 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.
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