Diagnosing Glaucoma Using Fundus Images

Abstract

This paper presents a novel approach for the diagnosis of glaucoma, a leading cause of blindness, through the analysis of fundus images. Glaucoma is often marked by an insidious onset, with symptoms such as ocular redness, rainbow-colored halos, and gradual vision loss emerging as the disease progresses, ultimately leading to optic nerve damage. Given the subtlety of its early manifestations—earning it the mark "silent thief of sight"—early detection is critical. This research leverages advanced image processing techniques, including 2D Gabor filtering and Circular Hough transform, to enhance the features within retinal images that are indicative of glaucomatous changes. By integrating image segmentation through thresholding and match filtering, the proposed system effectively identifies the optic disc, a pivotal step in assessing the disease's presence. The methodology outlined herein demonstrates significant improvements in precision over existing diagnostic methods. This study lays the groundwork for future advancements, with the ultimate goal of augmenting the precision and ease of glaucoma detection for early intervention, thereby preserving the vision and quality of life for patients at risk.