Description

Diabetic retinopathy is an ocular disease which cause blindness in the world among patients suffering from diabetes. The retinal image in RGB is represented in CIELAB color space to eliminate the noise. Then the image is clustered using K-mean clustering. The image is enhanced by using adaptive histogram equalization technique. Then the optic disk is marked using hough transform. After that we are classifying the exudates .whether it is hard or soft based on threshold value and edge energy. It is characterized by many pathologies, namely microaneurysms, hard exudates, soft exudates, hemorrhages, etc, among them presence of exudates is the prominent sign of non-proliferative DR. Both hard and soft exudates play a vital role in grading DR into different stages. we present an efficient method to identify and classify the exudates as hard and soft exudates. The retinal image in CIELAB color space is pre-processed to eliminate noise. Next, blood vessels network is eliminated to facilitate detection and elimination of optic disc. Optic disc is eliminated using Hough transform technique. The candidate exudates are then detected using k-means clustering technique. Finally, the exudates are classified as hard and soft exudates based on their edge energy and threshold. After that, the retinal image was clustered using k-mean clustering.For feature extraction the enhancement was taken by adaptive histogram equalization.Then the optic disk was marked using hough transform.