A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions

Diabetic burden around the world with a consequence of diabetic retinopathy can lead to permanent blindness in patients. Exudates detection in fundus images through an automated method is a vital task that has many applications in diabetic retinopathy screening. Realizing it important, a system being proposed in this paper automatically classifies exudates and non-exudates regions in retinal images. Presented technique is based on pre-processing for candidate lesion extraction, features extraction and classification. In pre-processing, Gabor filter is applied to the gray scale image which makes it useful for lesion enhancement. Segmentation of candidate lesion is based on mathematical morphology. A features set is selected for each candidate lesion using a combination of statistical and geometric features. Presented method is evaluated via publicly accessible datasets with the help of performance parameters such as true positive, false positive and area under curve for statistical analysis. Publicly available datasets such as e-ophtha, HRIS, MESSIDOR, DIARETDB1, VDIS, DRIVE, HRF and one local dataset are used to test the suggested system. The achieved results show an average AUC of 0.98 and accuracy as high as 98.58% which are substantially higher than the existing methods.