Abstract—Detection of Exudates in colour Fundus images. Diabetic Retinopathy is the major cause of blindness in many diabetic patients. Automatic < Final Year Projects > detection of exudates in retinal images can assist in early screening of Diabetic Retinopathy. Several techniques can achieve good performance on a good quality retinal images. But when the image is of low quality, we need a new method. In this paper, we presented a novel method for the detection of exudates in low quality retinal images. The colour retinal images are pre-processed by a hyperbolic median filter and then segmented using fuzzy c-means clustering algorithm. After segmenting the images, a set of features based on colour, size and texture are extracted. Then these features are optimized using Particle Swarm Optimization (PSO) technique. Finally the features are classified using a recursive Support Vector Machine (SVM) Classifier. The proposed method achieves an accuracy of 98% and predictivity of 98.5% for the identification of exudates.
sales on Site11,021