vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff545c1d0000002045040001000200 An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM) to segment the spot foreground (FG) from background (BG). The PFLICM improves fuzzy local information c means (FLICM) algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF), Probability of error (pe), Discrepancy distance (D) and Normal mean square error (NMSE). SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.
V.G. Biju1, P. Mythili2
College of Engineering Munnar, India1, School of Engineering, CUSAT, India2
Gridding, Spot Segmentation, Local Information, Spatial Information, Typicality, Clustering, Gene Expression
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 6 , Issue: 2 , Pages: 1104-1114 )
Date of Publication :
November 2015
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