Glaucoma Insights: a Visual Comparative Analysis of Fundus Images Across Diverse Datasets

Glaucoma is a major global health issue that can lead to irreversible vision loss if untreated. Early diagnosis and continuous monitoring are crucial to mitigating its impacts. Fundus images are necessary to evaluate, diagnose glaucoma and offer an in-depth perception of retinal structures and features indicative of the condition. The project aims to compare the features of fundus images from three widely recognized datasets, they are REFUGE, ODIR, and DRONIS-DB focusing on analyzing critical metrics such as pixel intensity, brightness, image channel analysis, edge mean, and saturation means. Advanced visualizations are employed to ensure a comprehensive analysis of the features. The main objective of the work is to provide a structured data-driven approach for feature extraction and visualization, enhancing our understanding. This project contributes to advancing diagnostic precision, supporting clinical decision-making, and enabling researchers to explore innovative solutions in ophthalmology by bridging the gap between medical imaging data and actionable insights.