Automated Microaneurysms (MAs) Detection in Digital Colour Fundus Images Using Matched Filter

Diabetic retinopathy (DR), one of the most common causes of blindness, is a retinal abnormality caused by high glucose in diabetic patients that leads to micro vascular complications. DR has five levels of severity, i.e. no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR and proliferative diabetic retinopathy (PDR). Microaneurysms (MAs), the first sign of NPDR, can be used as a pre-indicator of DR. However, a manual assessment on digital colour fundus images conducted by ophthalmologists is time consuming. This paper introduces a new algorithm for the automated microaneurysms (MAs) detection in digital colour fundus images using matched filter. Generally, the algorithm consists of four phases, namely green band extraction, MAs and blood vessels isolation, MAs and blood vessels detection, and blood vessels removal. To validate the developed algorithm, the results are compared with their ground truths and annotations using ROI based validation. This algorithm obtains an average sensitivity, specificity, accuracy, and false positive number of 91.0603%, 99.9752%, 99.9752% and 256.44 pixels, respectively. This indicates that the proposed algorithm successfully detects microaneurysms and is able to be implemented in a system for DR mass screening purposes.