Microaneurysm Detection in Digital Retinal Images Using Blood Vessel Segmentation and Profile Analysis

The number of diabetic patients are increasing nowadays. So the chance for diabetic retinopathic (DR) diseases are also increasing. From the different diabetic retinopathic diseases, microaneurysms (MAs) are the first detectable symptom of DR. This paper makes an attempt to MA detection using blood vessel segmentation and profile analysis. After the removal of connected blood vessels, the remaining regions are used for the exact detection of microaneurysms. Ramp analysis and peak detection step is performed on the profile of the maximum intensity regions and a set of values indicating the size, height and shape of the peaks are calculated. Detected candidates are classified using feature set and naive Bayes classifier. Score values are assigned to each detected microaneurysm regions. The results show that proposed method significantly reduces the number of false positives per image and the performance is evaluated using Free-response Receiver operating characteristics(FROC)curve and calculated sensitivity and specificity which is competitive to the existing methods.