Automated Detection and Classification of Diabetic Retinopathy using Morphological processing and Support Vector Machine

The Digital image processing helps the ophthalmologists in distinguishing the vascular irregularities in order to detect some disorders related to retina like Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma etc., which can cause visual impairments. The retinal fundus images of the patients are procured by capturing the fundus of the eye with a digital fondues camera. The manual method of observing several retinal fundus images by the ophthalmologists is time consuming. Therefore, a computer assisted automated method is very helpful. In this paper a method for DR detection by utilizing Fuzzy C-Means (FCM) clustering and morphological image processing is proposed. The image pre-processing includes image resizing, CLAHE, contrast adjustment, gray and green channel extraction from the color fundus image. The classification by Support Vector Machine (SVM) classifier using selected features achieves an Accuracy of 87.50%, Sensitivity of 83.33%, and Specificity of 90%.