A review analysis on early glaucoma detection using structural features

Glaucoma is an eye disease that might cause severe destruction and permanent blindness if not detected at an early stage. Glaucoma is also called silent thief of sight. Glaucoma can be detected using structural and functional features. Functional features are observed by visual field testing and to observe structural features Optical Coherence Tomography (OCT) and Fundus images are the most widely used medical imaging techniques. Optic nerve head (ONH), Retinal layers are the key source and most repeatable structural features to detect structural changes in the retina of glaucomatous eyes. This paper presents a review on different glaucoma detection techniques from clinical and machine learning perspectives. The paper also highlights the functional and structural features and their significance with respect to digital fundus and OCT images for glaucoma detection. It concludes that structural features are more precise for early glaucoma detection as compared to functional features. Moreover, using hybrid features in training classifiers and correlating results of both fundus and OCT images can yield more accurate results.