Automated Recognition of Optic Disc and Blood Vessels in Diabetic Fundoscopy Images Using Real-Time Image Analysis

Diabetic retinopathy (DR) is a sight-threatening condition associated with diabetes, characterized by damage to the retinal blood vessels. Key to the automation of DR staging is the identification of various symptoms directly or closely associated with retinal blood vessels, as well as the number of these symptoms in the four quadrants of the retina separated by the optic disc. Therefore, precise identification of the optic disc (OD) and blood vessels in fundus images is crucial for DR stage diagnosis but is often time-consuming and requires expert analysis. This study introduces a thresholding-based approach for the automated localization of the OD and the detection of blood vessels in fundus images of diabetic patients. Our algorithm is more robust than some deep learning-based algorithms, achieving more accurate results, particularly in advanced DR stages where the resemblance between various symptoms and blood vessels complicates the extraction of blood vessels. Additionally, our computer vision system can achieve OD localization and blood vessel segmentation in real time. The experimental results on a dataset selected by an ophthalmologist from a Kaggle dataset, ensuring data quality, show that the proposed algorithm can achieve an accuracy higher than 94% for both OD localization and blood vessel detection, outperforming some state-of-the-art algorithms.