摘要
Glaucoma is a group of ocular atrophy diseases that cause progressive vision loss by affecting the optic nerve.Because of its asymptomatic nature,glaucoma has become the leading cause of human blindness worldwide.In this paper,a novel computer-aided diagnosis(CAD)approach for glaucomatous retinal image classification has been introduced.It extracts graph-based texture features from structurally improved fundus images using discrete wavelet-transformation(DWT)and deterministic tree-walk(DTW)procedures.Retinal images are considered from both public repositories and eye hospitals.Images are enhanced with image-specific luminance and gradient transitions for both contrast and texture improvement.The enhanced images are mapped into undirected graphs using DTW trajectories formed by the image’s wavelet coefficients.Graph-based features are extracted fromthese graphs to capture image texture patterns.Machine learning(ML)classifiers use these features to label retinal images.This approach has attained an accuracy range of 93.5%to 100%,82.1%to 99.3%,95.4%to 100%,83.3%to 96.6%,77.7%to 88.8%,and 91.4%to 100%on the ACRIMA,ORIGA,RIM-ONE,Drishti,HRF,and HOSPITAL datasets,respectively.The major strength of this approach is texture pattern identification using various topological graphs.It has achieved optimal performance with SVM and RF classifiers using biorthogonal DWT combinations on both public and patients’fundus datasets.The classification performance of the DWT-DTW approach is on par with the contemporary state-of-the-art methods,which can be helpful for ophthalmologists in glaucoma screening.
