Damaging of anatomical structures like vessels in eyes, kidneys, heart and nervous systems occurs in people suffering with diabetes. Diabetic Retinopathy (DR) is an important hurdle in diabetic people and it is main root cause of lesion formation in retina. Bright Lesions (BLs) are preliminary clinical sign of DR. Early BLs detection can help avoiding blindness. The severity can be recognized based on increasing number of BLs formation in the color fundus image. Manually diagnosing large amounts of images is time consuming. So a computer-assisted DR evaluation and BLs detection system is proposed. In this paper initially curvelet enhancement is done for unwanted patches removal. For BLs detection, the Optic Disk (OD) and vessel structures are segmented and eliminated by thresholding techniques. Because OD and vessels are obstacles for exudates exact recognition and DR severity grading. Few images were obtained from ABC imaging centre located in Vijayawada, Andhra Pradesh for testing of the proposed. Publicly available databases are also used for DR severity testing. The Support Vector Machine classifier (SVM) is a supervised learning technique separated fundus images in various levels of DR based on extracted feature set. The proposed system obtained the better results compared to the existing techniques in terms of statistical measures sensitivity, specificity and accuracy.