Segmentation of Blood Vessels and Optic Disc in Fundus Images

Diabetic retinopathy also known as diabetic eye disease, is when damage occurs to the retina due to diabetes. It can eventually lead to blindness. By analyzing and detecting vasculature structures in retinal image the diabetes can be detected in advanced stages by comparing its states of retinal blood vessels. In blood vessel classification approach computer based retinal image analysis can be used to extract the retinal image vessels. Stationary wavelet transform (SWT) are used to extract the features from the fundus image and classification can be performed using Support Vector Machine(SVM). SVM has become an essential machine learning method for the detection and classification of particular patterns in medical images. It is used in a wide range of applications for its ability to detect patterns in experimental databases. If the vessels are present, then it is extracted by using segmentation. Mathematical morphology and K-means clustering is used to segment the vessels. To enhance the blood vessels and suppress the background information, smoothing operation can be performed on the retinal image using mathematical morphology. Then the enhanced image is segmented using K-means clustering algorithm to detect the diseases easily.