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Glaucoma is a neurodegenerative disease presents with retinal nerve fiber layer (RNFL) defects. We apply machine learning classifiers on the color information of the RNFL to differentiate between intact RNFL (i-RNFL) and RNFL defect (d-RNFL) on optic disc photographs (DPs). DPs from individuals with and without glaucoma were collected. Then, a semi-circle was automatically marked on the DPs, to label i-RNFL versus d-RNFL. RGB intensities and other color spaces of two profiles were collected. Five-fold cross validation is used to compare classification efficiency of five classifiers. A total of 2,051 profiles from 89, 32 and 15 DPs from patients with glaucoma, glaucoma suspects and control subjects were collected. There were 702 and 175 points of d-RNFL and 940 and 234 of i-RNFL in the training and test sets. In the training set, the 3 best classifiers using RGB intensities were fine Gaussian support vector machine (SVM), medium k-Nearest Neighbor and ensemble RUSBoosted Trees, with accuracies of 81.8%, 79.4% and 79.2%. The performance of the fine Gaussian SVM was similar between RGB and other color spaces. In the test set, the highest sensitivity (71.4%) and specificity (88.5%) were archived using RGB and the combination of RGB and Cb and Cr.

Glaucoma is the second leading cause of blindness worldwide. This paper proposes an automated glaucoma screening method using retinal fundus images via the ensemble technique to fuse the results of different classification networks and the result of each classification network was fed as an input to a simple artificial neural network (ANN) to obtain the final result. Three public datasets, i.e., ORIGA-650, RIM-ONE R3, and DRISHTI-GS were used for training and evaluating the performance of the proposed network. The experimental results showed that the proposed network outperformed other state-of-art glaucoma screening algorithms with AUC of 0.94. Our proposed algorithms showed promising potential as a medical support system for glaucoma screening especially in low resource countries.

SIGNIFICANCE Head-mounted video display systems and image processing as a means of enhancing low vision are ideas that have been around for more than 20 years. Recent developments in virtual and augmented reality technology and software have opened up new research opportunities that will lead to benefits for low vision patients. Since the Visionics low vision enhancement system (LVES), the first head-mounted video display LVES, was engineered 20 years ago, various other devices have come and gone with a recent resurgence of the technology over the past few years. In this article, we discuss the history of the development of LVESs, describe the current state of available technology by outlining existing systems, and explore future innovation and research in this area. Although LVESs have now been around for more than two decades, there is still much that remains to be explored. With the growing popularity and availability of virtual reality and augmented reality technologies, we can now integrate these methods within low vision rehabilitation to conduct more research on customized contrast-enhancement strategies, image motion compensation, image-remapping strategies, and binocular disparity, all while incorporating eye-tracking capabilities. Future research should use this available technology and knowledge to learn more about the visual system in the low vision patient and extract this new information to create prescribable vision enhancement solutions for the visually impaired individual.