This article proposes an automatic image processing method that can be an effective diagnostic tool to detect and grade the severity of diabetic retinopathy. This computer vision-based algorithm imitates the logic and medical sense used by ophthalmologist in detecting the abnormality and its location in the image for grading the severity of the disease. The detection is based on finding abnormalities, such as exudates and red lesions, and the grading is based on the location of these abnormalities in the image referred with its distance from the macula. Accordingly the entire image has been divided into regions and occurrence of abnormalities in these regions indicates the severity of the disease. The methodology stresses on two major factors, accuracy of the results and computation time. It is found that both these design parameters are nicely achieved in the proposed method. The experimental results indicate that overall accuracy over 90% can be achieved in the proposed method and also significantly reduce the computational complexity in this region-based approach.