Detection of Exudates in Diabetic Retinopathy Images using Laplacian Kernel Induced Spatial FCM Clustering Algorithm

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.