Detection of Macular Edema in Acute Phase through Optical Coherence Tomography using Local Binary Pattern Feature

Abstract

Diabetic macular edema is one of the major causes for blindness and can be detected early by using OCT imaging technique. Optical Coherence Tomography is a fundamentally novel technique of optical imaging modality. In this paper we are mainly focused to extract local features by using different methods for detection of DME followed by its classification. Used the image processing techniques of removal of the speckle noise, proper alignment followed by local feature extraction by using Local binary pattern .Then extracted features are classified using SVM classifier. In this study for testing 30 OCT images datasets in that 15 normal and 15 DME images were used for classification. We have used SVM classifier it gives the best results, likes 98, 98 and 98.77 for specificity, sensitivity and accuracy respectively. Here SVM classifier with LBP features detected the diseases with an accuracy of 98.77% our proposed method shows that the SVM classifier with LBP features gives a better improved performance.