Hierarchical classification and grading of diabetic macular edema using texture features

Diabetic macula edema (DME) is an eye pathology, a complication of diabetic retinopathy, which is caused due to the presence of exudates around the fovea. In this paper, an automated method for robust classification and grading of DME is presented. The algorithm proposed presents a computerized method of processing the images in the database, extracting texture features in both spatial domain and wavelet domain from sub-regions with a specified radius around the macula. Unlike other well-known approaches of machine learning classifiers, we propose a method that processes the specific sub-regions of interests instead of the whole image which makes it computationally efficient. Grading of the disease into 3 stages namely normal, moderate and severe diabetic macular edema based on severity is done in a hierarchal manner.