Micrarray Image Segmentation Using Protracted K-Means Net Algorithm in Enhancement of Accuracy And Robustness

Abstract

Microarray is the technology, which is used in various biological studies mainly in the field of image segmentation. The analysis of data in microarray has provide the result in the terms of accuracy and robustness.In this paper presents theprotracted K-means NeT an efficient technique for improving robustness and accuracy in images.The metrics which are consider for analysis are SNR, coefficient of variation, coefficient of determination and standard deviation.Protracted K-means NeT algorithm, takes advantage of the spatial information around each spot of interest, calculating the of the found foreground F through existing K-means algorithm. Subsequently the method analyses the neighbors at the boundary of F carefully, before making the final decision of including some of the noisy pixels to F. Here, Jeffrey’s divergence metric is projected for dinding the intensity values. Experimental results and analysis, and comparison of the proposed method namely protracted K-means NeT Algorithm with existing methods such as K-Means , GMM and Multifeature shows the promising results such as 95%of SNR 1029.4 SSD 90% of CV, 521sec of MAE.