19 February 2018 Automated classification and quantitative analysis of arterial and venous vessels in fundus images
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Proceedings Volume 10474, Ophthalmic Technologies XXVIII; 1047426 (2018) https://doi.org/10.1117/12.2290121
Event: SPIE BiOS, 2018, San Francisco, California, United States
Abstract
It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).
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Minhaj Alam, Taeyoon Son, Devrim Toslak, Jennifer I. Lim, Xincheng Yao, "Automated classification and quantitative analysis of arterial and venous vessels in fundus images", Proc. SPIE 10474, Ophthalmic Technologies XXVIII, 1047426 (19 February 2018); doi: 10.1117/12.2290121; https://doi.org/10.1117/12.2290121
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