Diabetic Retinopathy (DR) is a predominant factor leading to blindness for decades. The main reason for loss of vision is the damage to vasculature in the retina. The earliest signs of DR are microaneurysms which show up as tiny red spots on the retina. Early detection of this indicator helps the ophthalmologists to detect DR, which helps in preventing blindness. In this work, a series of image processing algorithms including pre-processing, and coarse segmentation using mathematical morphology are employed to detect initial candidates for microaneurysms. This is followed by fine segmentation for which a set of optimal features is estimated using Particle Swarm Optimization (PSO). Classification performance of naïve Bayes and Support Vector Machine (SVM) are compared. The set of 19 features selected using PSO-SVM has led to an accuracy of 99.92% compared to the PSO-naïve Bayes with 22 features and an accuracy of 93.31%. The proposed system could be employed for accurate and fast detection of microaneurysms and thereby would considerably lower the workload and time spent by ophthalmologists.
cited By 0; Conference of 2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017 ; Conference Date: 22 March 2017 Through 24 March 2017; Conference Code:134757
S. N. Kumar, Dinesh, D., Siddharth, T., Ramkumar, S., Nikhill, S., and Dr. Lavanya R., “Selection of Features Using Particle Swarm Optimization for Microaneurysm Detection in Fundus Images”, in Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, 2018, vol. 2018-January, pp. 140-144.