Pratt, H, Williams, B, Ku, J, Vas, C, McCann, E, Al-Bander, B ORCID: https://orcid.org/0000-0002-2518-7364, Zhao, Y, Coenen, F and Zheng, Y (2017) Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography. Journal of Imaging, 4 (1). 4 - 4.

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Abstract

The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease

Item Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Uncontrolled Keywords: medical image analysis; machine learning; convolutional neural networks; retinal imaging; retinal vessels; fundus photography; vessel classification
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 02 Aug 2022 14:57
Last Modified: 02 Aug 2022 14:57
URI: https://eprints.keele.ac.uk/id/eprint/11221

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