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An Analysis and Comparison of Quality Index Using Clustering Techniques for Spot Detection in Noisy Microarray Images
A. Sri Nagesh, G.P.Saradhi Varma, A.Govardhan, B.Raveendra Babu
Pages - 504 - 511     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Microarray Image, Genes, Spot Segmentation, Morphological Operator, Fuzzy K-Means, Fuzzy C-means, Enhanced fuzzy C-means Clustering (EFCM).
In this paper, the proposed approach consists of mainly three important steps: preprocessing, gridding and segmentation of micro array images. Initially, the microarray image is preprocessed using filtering and morphological operators and it is given for gridding to fit a grid on the images using hill-climbing algorithm. Subsequently, the segmentation is carried out using the fuzzy c-means clustering. Initially the enhanced fuzzy c-means clustering algorithm (EFCMC) is implemented to effectively clustering the image whether the image may be affected by the noises or not. Then, the EFCM method was employed the real microarray images and noisy microarray images in order to investigate the efficiency of the segmentation. Finally, the segmentation efficiency of the proposed approach was compared with the various algorithms in terms of quality index and the obtained results ensures that the performance efficiency of the proposed algorithm was improved in term of quality index rather than other algorithms.
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Mr. A. Sri Nagesh
- India
Mr. G.P.Saradhi Varma
- India
Mr. A.Govardhan
- India
Mr. B.Raveendra Babu
- India

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