An Optimised Morphological Image Processing Method suitable for the Early Detection of Diabetic Retinopathy

Diabetic Retinopathy is a leading cause of irreversible blindness worldwide. However, early detection and timely treatment can significantly reduce morbidity and further vision loss. This work presents a lightweight automatic image processing / decision support system to detect early-stage diabetic retinopathy that would be suitable for implementation on a mobile device. In this study, we present a novel automatic and computationally uncomplicated extraction method to derive microaneurysms (a significant feature of early diabetic retinopathy). The proposed method is a sequence of morphological image processing operations including edge detection, boundary analysis, black top hat, image segmentation and logical operations. The work utilised images from the DIARETDBO, DIARETDB1, KAGGLE and MESSIDOR databases with classification further verified by a qualified ophthalmic diabetic specialist. The microaneurysm detection method returned an accuracy of 87%.