Predictive Diagnosis of Glaucoma Based on Analysis of Focal Notching along the Neuro-Retinal Rim Using Machine Learning

Abstract

Automatic evaluation of the retinal fundus image is regarded as one of the most important future tools for early detection and treatment of progressive eye diseases like glaucoma. Glaucoma leads to progressive degeneration of vision which is characterized by shape deformation of the optic cup associated with focal notching, wherein the degeneration of the blood vessels results in the formation of a notch along the neuroretinal rim. In this study, we have developed a methodology for automated prediction of glaucoma based on feature analysis of the focal notching along the neuroretinal rim and cup to disc ratio values. This procedure has three phases: the first phase segments the optic disc and cup by suppressing the blood vessels with dynamic thresholding; the second phase computes the neuroretinal rim width to detect the presence and direction of notching by the conventional ISNT rule apart from calculating the cup-to-disc ratio from the color fundus image (CFI); the third phase uses linear support vector based machine learning algorithm by integrating extracted parameters as features for classification of CFIs into glaucomatous or normal. The algorithm outputs have been evaluated on a freely available database of 101 images, each marked with decision of five glaucoma expert ophthalmologists, thereby returning an accuracy rate of 87.128%.