Exudate characterization to diagnose diabetic retinopathy using generalized method.

Globally in recent days, the potential risk for patients with diabetes mellitus is the prevalence of diabetic retinopathy, which is a silent disease with no early symptoms and is the imperative cause of vision loss. An early diagnosis can be used to prevent for visual loss and blindness. In the regular screening process, assistance of computerized diagnosis can considerably minimize an ophthalmologists work and improve inter and intra viewer variability. A generalized method of semi automatic exudates characterization to diagnose diabetic retinopathy with exudates screening system of retinal image is presented in this paper. This system uses morphological processing based retinal blood vessel suppression, Semi automatic masking of optic disc structure and morphological component analysis based texture enhancement followed by segmentation and Adaptive Neuro-Fuzzy Inference System (ANFIS) based classification method to discriminate between normal and pathological retinal structures. The novelty of this system relies on the appropriate sequential application of exclusive image processing techniques in synergy with ANFIS classifier to improve the accuracy of exudate lesions characterization. The performance of the system has been evaluated by comparing it with various state of the art existing methods in terms of several performance metrics such as Accuracy, Average error rate, F-Score and Kappa value. The obtained numerical results prove that the proposed system with ANFIS classifier demonstrated superior performance in identification of exudate lesions. [ABSTRACT FROM AUTHOR]