tDiabetic retinopathy is the major cause of blindness in the world. It has been shown that early diagnosiscan play a major role in prevention of visual loss and blindness. This diagnosis can be made throughregular screening and timely treatment. Besides, automation of this process can significantly reduce thework of ophthalmologists and alleviate inter and intra observer variability. This paper provides a fullyautomated diabetic retinopathy screening system with the ability of retinal image quality assessment. Thenovelty of the proposed method lies in the use of Morphological Component Analysis (MCA) algorithmto discriminate between normal and pathological retinal structures. To this end, first a pre-screeningalgorithm is used to assess the quality of retinal images. If the quality of the image is not satisfactory, it isexamined by an ophthalmologist and must be recaptured if necessary. Otherwise, the image is processedfor diabetic retinopathy detection. In this stage, normal and pathological structures of the retinal imageare separated by MCA algorithm. Finally, the normal and abnormal retinal images are distinguished bystatistical features of the retinal lesions. Our proposed system achieved 92.01% sensitivity and 95.45%specificity on the Messidor dataset which is a remarkable result in comparison with previous work.

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