Abstract
Several techniques have been employed to detect glaucoma from optic discs. Some techniques involve the use of the optic cup-to-disc ratio (CDR) while others use the neuro-retinal rim width of the optic disc. In this work, we use the area occupied by segmented blood vessels from fundus images to detect glaucoma. Blood vessels segmentation is done using an improved U-net Convolutional Neural Network (CNN). The area occupied by the blood vessels is then extracted and used to diagnose glaucoma. The technique is tested on the DR-HAGIS database and the HRF database. We compare our result with a similar method called the ISNT-ratio which involves the use of the Inferior, Superior, Nasal and Temporal neuro- retina rims. The ISNT-ratio is expressed as the ratio of the sum of blood vessels in the Inferior and the Superior to the sum of blood vessels in the Nasal and Temporal. Our results demonstrate a more reliable, stable and efficient method of detecting glaucoma from segmented blood vessels. Our results also show that segmented blood vessels from healthy fundus images cover more area than those from glaucomatous and diabetic fundus images.
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Oluwatobi, J.A., Mabuza-Hocquet, G., Nelwamondo, F.V. (2021). The Use of Area Covered by Blood Vessels in Fundus Images to Detect Glaucoma. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_35
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