A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods.

Ocular imaging instruments, such as Confocal Scanning Laser Ophthalmoscopy (CSLO), captures high-quality images of the optic disc (also known as optic nerve head) that help clinicians to diagnose glaucoma. We present an integrated data analytics framework to aid clinicians in interpreting CSLO optic nerve images to diagnose and monitor the progression of glaucoma. To distinguish between healthy and glaucomatous optic discs, our framework derives shape information from CSLO images using image processing (Zernike moment method), selects salient features (hybrid feature selection), and then trains image classifiers (Multilayer Perceptron, Support Vector Machine, Bayesian Network). To monitor glaucoma progression over time, our framework uses a mathematical model of the optic disc to extract morphological features from CSLO images and applies clustering (Self-Organizing Maps) to visualize subtypes of glaucomatous optic disc damage. We contend that our data analytics framework offers an automated and objective analysis of optic nerve images that can potentially support both diagnosis and monitoring of glaucoma. We validated our framework with CSLO optic nerve images and our data analytics approach detected glaucomatous optic discs with a sensitivity of 0.86, a specificity of 0.80, an accuracy of 0.838, and an AUROC of 0.913 with a Bayesian network classifier using the optimal subset of Zernike features (six moments). Furthermore, our framework identified, using morphological features, five clusters of CSLO images, where each cluster stands for a subtype of optic nerve damage (two healthy subtypes and three glaucoma subtypes). The characteristics of each cluster-the subtype of the image-were determined by experts who examined the morphology of the images within each cluster and provided subtype characteristics to each cluster.