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J. Imaging 2018, 4(1), 4; doi:10.3390/jimaging4010004

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

1
Department of Eye and Vision Science, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 3BX, UK
2
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK
3
Department of Electrical Engineering, University of Liverpool, Liverpool L69 3BX, UK
4
Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, China
5
St Paul’s Eye Unit, Liverpool Royal University Hospital, Liverpool L69 3BX, UK
This paper is an extended version of our paper published in: Pratt, H.;Williams, B.M.; Ku, J.; Coenen, F.; Zheng, Y. Automatic Detection and Identification of Retinal Vessel Junctions in Colour Fundus Photography. In Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings; Springer International Publishing: Cham, Switzerland, 2017; pp. 27–37.
Current address: Department of Eye and Vision Science, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK.
*
Author to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 12 December 2017 / Accepted: 14 December 2017 / Published: 22 December 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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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. View Full-Text
Keywords: medical image analysis; machine learning; convolutional neural networks; retinal imaging; retinal vessels; fundus photography; vessel classification medical image analysis; machine learning; convolutional neural networks; retinal imaging; retinal vessels; fundus photography; vessel classification
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Pratt, H.; Williams, B.M.; Ku, J.Y.; Vas, C.; McCann, E.; Al-Bander, B.; Zhao, Y.; Coenen, F.; Zheng, Y. Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography. J. Imaging 2018, 4, 4.

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