Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 15, Pages: 1-6
R. Ravindraiah1*, S. Chandra Mohan Reddy2 and P. Rajendra Prasad1
1MITS, Angallu(V), Madanapalle - 517325, Andhra Pradesh, India; [email protected], [email protected] 2Department of ECE., JNTUA College of Engineering, Pulivendula - 516390, Andhra Pradesh, India; [email protected]
*Author of Corresponding: R. Ravindraiah MITS, Angallu(V), Madanapalle - 517325, Andhra Pradesh, India; [email protected]
Diabetic Retinopathy (DR) is the consequence of micro-vascular retinal changes triggered by diabetes which can cause vision loss if not treated in a timely manner. The major sign of Diabetic Retinopathy are the presence of Exudates. This paper demonstrates a complete framework for the detection of Hard Exudates in Retinopathy images. This paper presents laplacian kernel and it is induced into the kernel spatial FCM clustering algorithm for the segmentation of retinal fundus images. In general, FCM and KFCM algorithms very sensitive to noise and other imaging artefacts because it doesn’t have spatial information. To overcome this problem, we presented Laplacian kernel spatial FCM which incorporates spatial information into its objective function and the fuzzy membership function. The performance of our proposed algorithm evaluated on different Diabetic Retinopathy images. The presented methodology is assessed using statistical measures like Sensitivity and Specificity.
Keywords: Diabetic Retinopathy (DR), Fuzzy C Means Clustering algorithm (FCM), Kernel induced Fuzzy C Means Clustering algorithm (KFCM), Kernel induced Fuzzy C Means Clustering algorithm with induces Spatial constraint (KSFCM)
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