Automatic classification of diabetic macular edema using a modified completed Local Binary Pattern (CLBP)

Abstract

Diabetic macular edema is the leading cause of visual loss for patients with diabetic retinopathy, a complication of diabetes. Early screening and treatment has been shown to prevent blindness in diabetic retinopathy and diabetic macular edema. The Early Treatment Diabetic Retinopathy Study (ETDRS) and the Diabetic Macular Edema Disease Severity Scale are the common screening standards based on the distance of exudates from the fovea. Instead of focusing on the macula region, this research adopts a global approach using texture classification to grade the fundus images into three stages: normal, moderate diabetic macular edema and severe diabetic macular edema. The proposed algorithm starts with a modified completed Local Binary Pattern (CLBP) to extract the image local gray level for all RGB channels. The obtained feature vector will then be fed into a multiclass Support Vector Machine (SVM) for classification. The 100 fundus images selected to be utilized for training and testing set were taken from MESSIDOR and these images were reviewed by an ophthalmologist for cross-validation. The algorithm using the CLBP demonstrates a sensitivity of 67% with a specificity of 30% while the proposed modified CLBP yields a higher sensitivity and specificity of 80% and 70% respectively.