Improving Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images using Feature Optimization

Diabetic retinopathy usually does not presents symptoms in an early stage until it gets to a severe stage. An early stage of diabetic retinopathy is associated with the presence of microaneurysms (MAs). The occurrence of blindness can be reduced significantly if MAs are detected. This paper presented an approach to improve automatic MAs detection using feature optimisation. Candidate MAs are detected using mathematic morphological techniques. Originally 20 features are presented. To verify the relevance of all original features, a feature optimisation process is performed. The optimal feature set is searched by a machine learning approach, like naive Bayes and support vector machine classifier. Hand-drawn ground-truth images from expert ophthalmologists are used to measure the performance evaluation. The results showed that the proposed optimal feature set could significantly improve MA detection.