Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Pratt, Harry; Williams, Bryan M.; Ku, Jae Yee; Vas, Charles; McCann, Emma; Al-Bander, Baidaa; Zhao, Yitian; Coenen, Frans; Zheng, Yalin

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

Citation

Pratt, H., Williams, B. M., Ku, J. Y., Vas, C., McCann, E., Al-Bander, B., Zhao, Y., Coenen, F., & Zheng, Y. (2018). Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography. Journal of Imaging, 4(1), 4 - 4. https://doi.org/10.3390/jimaging4010004

• Journal Article Type: Article
• Acceptance Date: Dec 14, 2017
• Publication Date: 2018-01
• Publicly Available Date: May 30, 2023
• Journal: JOURNAL OF IMAGING
• Electronic ISSN: 2313-433X
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 1
• Article Number: ARTN 4
• Pages: 4 - 4
• ISBN: 2313433X
• DOI: https://doi.org/10.3390/jimaging4010004
• Keywords: medical image analysis; machine learning; convolutional neural networks; retinal imaging; retinal vessels; fundus photography; vessel classification
• Public URL: https://keele-repository.worktribe.com/output/423847
• Publisher URL: https://www.mdpi.com/2313-433X/4/1/4