Abstract Diabetic Retinopathy (DR) is a diabetical retinal syndrom. Large number of patients have been suffered from blindness due to DR as compared to other diseases. Priliminary detection of DR is a critical quest of medical image processing. Retinal Biomarkers are termed as Microaneurysms (MAs), Haemorrhages (HMAs) and Exudates (EXs) that are helpful to grade Non-Proliferative DR (NPDR) at different stages. This research work contributes an automatic design for the retinal lesions screening to grade DR system. The system is comprised of unique preprocessing determination of biomarkers and formulation of profile set for classification. During preprocessing, Contrast Limited Adaptive Histogram Equalization (CLAHE) is utilized and Independent Component Analysis (ICA) is extended with Curve Fitting Technique (CFT) to eliminate blood vessels and optic disc as well as to detect biomarkers from the digital retinal image. Subsequent, NPDR lesions based distinct eleven features are deduced for the purpose of classification. Experiments are performed using a fundus image database. The proposed method is appropriate for initial grading of DR.

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