A Novel Approach for Detection of Microaneurysms in Diabetic Retinopathy Disease from Retinal Fundus Images

Diabetic Retinopathy (DR) is a leading cause of blindness in human beings aged between 20 to 74 years. It has a great influence on the patient and society because it normally influences humans in their most gainful years. Early detection in DR is very difficult which is not detected by human beings. Many algorithms and techniques were established to detect DR. These techniques faced the problems such as increasing sensitivity, specificity and accuracy. To overcome those problems we have to introduce an effective image processing algorithms for increasing performances and also easily identify the DR diseases. One of the most challenging tasks in screening is automatic detection of Microaneurysms (MAs). This paper presents a new approach to detect MAs. Our proposed work consists of preprocessing, blood vessel segmentation (FPCM), fovea localization, fovea elimination, feature extraction and classification (Neuro-Fuzzy). Neuro-Fuzzy is a combined version of neural networks and fuzzy logical models. Experiments are conducted using MATLAB simulation tool. Using MESSIDOR database for our experiments which provides efficient and effective results in sensitivity, specificity, correct classification and detection rate (accuracy) and precision.