Abstract-Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy and can lead to irreversible vision loss which can be assessed by detecting exudates (a type of bright lesion) in color fundus images. In this approach, an automatic and efficient method for the detection and classification of DME severity level is proposed. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Clustering-based method is used to segment exudates. Using k-means clustering algorithm, the severity level of DME is determined. The performance of the proposed methodology and features are evaluated against several publicly available datasets
