Detection of Diabetic Retinopathy Using Discrete Wavelet Transform with Discrete Meyer in Retinal Images

Abstract

One of the major complicated issues for extensive term diabetic aspirant is diabetic retinopathy (DR) which is an eye retinal syndrome, leads to blindness. The presence of exudates detects the disease, which can be prevented in the early stages by regular screening. Exudates can be automatically detected through inspecting digital retinal image. To detect the exudates for diagnosis the author proposed an algorithm called K-means Kernel support vector machine Radial basis function (KKR) approach, by the following main stages: extracting vessel and removal of optic disc followed by pre-processing, exudates detection and post processing. Wavelet dependent edge enhancement is used for dark portion separation of exudates in the retinal image by optically designed Wideband bandpass filter. Wavelet toolbox of MATLAB 2018a is used in this KKR algorithm. Statistical and structural texture features can be obtained using K-means segmentation process by integrating Local Binary Pattern (LBP) with Region Of Interest (ROI). Some features are selected and used Neural Network along with Radial Basis Function (RBF) to classify further. The KKR algorithm uses 80 fundus images from DIARETDB1 database and parameters are analyzed such as specificity, sensitivity and accuracy. The results obtained from proposed KKR algorithm have specificity of 81.57%, sensitivity of 87.56% and accuracy of 97.94% respectively.