Diabetic Retinopathy is a major cause of blindness. It is mainly due to the development of abnormal new blood vessels in the retina. In this approach, we proposed an efficient method to detect the abnormal new blood vessels. The retinal images are pre-processed using Adaptive Histogram Equalization (AHE) and the blood vessels are enhanced by applying Top-hat and Bottom-hat transforms. The enhanced image is segmented using Fuzzy C Means Clustering (FCM) technique. Features based on shape, brightness, position and contrast are extracted from the segmented image and classified as normal or abnormal using K Nearest Neighbour (KNN) Classifier. The performance was evaluated on DRIVE and MESSIDOR database and an accuracy of 96.5% was obtained.