Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding

• Published in: Diagnostics (Basel) 2023-06, Vol.13 (13), p.2231
• Main Authors: Huda, Noor Ul, Salam, Anum Abdul, Alghamdi, Norah Saleh, Zeb, Jahan, Akram, Muhammad Usman
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Automation; autonomous disease detection; Banks (Finance); biomedical imaging; Blood vessels; Datasets; Diabetes; Diabetic retinopathy; Diabetics; Diagnosis; Fractals; fundus image analysis; image processing; proliferative diabetic retinopathy; Retina; Spectrum analysis; Support vector machines
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13132231

Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. In this paper, a novel method is proposed for the detection and grading of neovascularization. The proposed system first performs pre-processing on input retinal images to enhance the vascular pattern, followed by blood vessel segmentation and optic disc localization. Then various features are tested on the candidate regions with different thresholds. In this way, positive and negative advanced diabetic retinopathy cases are separated. Optic disc coordinates are applied for the grading of neovascularization as NVD or NVE. The proposed algorithm improves the quality of automated diagnostic systems by eliminating normal blood vessels and exudates that might cause hindrances in accurate disease detection, thus resulting in more accurate detection of abnormal blood vessels. The evaluation of the proposed system has been carried out using performance parameters such as sensitivity, specificity, accuracy, and positive predictive value (PPV) on a publicly available standard retinal image database and one of the locally available databases. The proposed algorithm gives an accuracy of 98.5% and PPV of 99.8% on MESSIDOR and an accuracy of 96.5% and PPV of 100% on the local database.