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Publication Type : Journal Article
Publisher : Journal of Theoretical and Applied Information Technology, 88 (3), pp. 535-540,
Source : Journal of Theoretical and Applied Information Technology, 88 (3), pp. 535-540, 2016.
Keywords : Artificial Bee-colony, Clustering, Fundus camera, Fuzzy C-Means, Support Vector Machine
Campus : Mysuru
School : School of Arts and Sciences
Department : Computer Science
Year : 2016
Abstract : Retinal blood vessel Extraction in retinal images allows early diagnosis of disease and is useful in detecting ocular disorders and helps in laser surgery. Automating this process provides several benefits including minimizing subjectivity and eliminating a painstaking. This paper proposes an automated retinal blood vessel segmentation approach based on Fuzzy C-Means (FCM) clustering and then performed extraction using Artificial Bee-colony (ABC) to improve the accuracy of segmented image. FCM allocate the values of membership to the pixels instead of separating the pixels as in hard clustering problem and the clustering is optimized using ABC swarm based optimization algorithm, finally the system classify the images according to the level of damage in blood vessel using support vector machine (SVM). The performance was evaluated on DRIVE database and an accuracy of 96.35% was obtained. © 2005 - 2016 JATIT amp; LLS. All rights reserved.
Cite this Research Publication : Kavya, K., Dechamma, M.G., Santhosh Kumar, B.J., "Extraction of retinal blood vessel using Artificial Bee-colony optimization, " Journal of Theoretical and Applied Information Technology, 88 (3), pp. 535-540, 2016.