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Automatic detection and identification of retinal vessel junctions in colour fundus photography

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Automatic detection and identification of retinal vessel junctions in colour fundus photography. / Pratt, Harry; Williams, Bryan M.; Ku, Jae et al.
Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. ed. / Victor Gonzalez-Castro; Maria Valdes Hernandez. Springer-Verlag, 2017. p. 27-37 (Communications in Computer and Information Science; Vol. 723).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Pratt, H, Williams, BM, Ku, J, Coenen, F & Zheng, Y 2017, Automatic detection and identification of retinal vessel junctions in colour fundus photography. in V Gonzalez-Castro & M Valdes Hernandez (eds), Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. Communications in Computer and Information Science, vol. 723, Springer-Verlag, pp. 27-37, 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Edinburgh, United Kingdom, 11/07/17. https://doi.org/10.1007/978-3-319-60964-5_3

APA

Pratt, H., Williams, B. M., Ku, J., Coenen, F., & Zheng, Y. (2017). Automatic detection and identification of retinal vessel junctions in colour fundus photography. In V. Gonzalez-Castro, & M. Valdes Hernandez (Eds.), Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings (pp. 27-37). (Communications in Computer and Information Science; Vol. 723). Springer-Verlag. https://doi.org/10.1007/978-3-319-60964-5_3

Vancouver

Pratt H, Williams BM, Ku J, Coenen F, Zheng Y. Automatic detection and identification of retinal vessel junctions in colour fundus photography. In Gonzalez-Castro V, Valdes Hernandez M, editors, Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. Springer-Verlag. 2017. p. 27-37. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-60964-5_3

Author

Pratt, Harry ; Williams, Bryan M. ; Ku, Jae et al. / Automatic detection and identification of retinal vessel junctions in colour fundus photography. Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. editor / Victor Gonzalez-Castro ; Maria Valdes Hernandez. Springer-Verlag, 2017. pp. 27-37 (Communications in Computer and Information Science).

Bibtex

@inproceedings{0fada4c367c548fe92a3dd445772d3f8,
title = "Automatic detection and identification of retinal vessel junctions in colour fundus photography",
abstract = "The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.",
keywords = "Convolutional neural networks, Retinal imaging, Retinal vessels fundus photography, Vessel classification",
author = "Harry Pratt and Williams, {Bryan M.} and Jae Ku and Frans Coenen and Yalin Zheng",
year = "2017",
month = jun,
day = "22",
doi = "10.1007/978-3-319-60964-5_3",
language = "English",
isbn = "9783319609638",
series = "Communications in Computer and Information Science",
publisher = "Springer-Verlag",
pages = "27--37",
editor = "Victor Gonzalez-Castro and {Valdes Hernandez}, Maria",
booktitle = "Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings",
note = "21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 ; Conference date: 11-07-2017 Through 13-07-2017",

}

RIS

TY - GEN

T1 - Automatic detection and identification of retinal vessel junctions in colour fundus photography

AU - Pratt, Harry

AU - Williams, Bryan M.

AU - Ku, Jae

AU - Coenen, Frans

AU - Zheng, Yalin

PY - 2017/6/22

Y1 - 2017/6/22

N2 - The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.

AB - The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.

KW - Convolutional neural networks

KW - Retinal imaging

KW - Retinal vessels fundus photography

KW - Vessel classification

U2 - 10.1007/978-3-319-60964-5_3

DO - 10.1007/978-3-319-60964-5_3

M3 - Conference contribution/Paper

AN - SCOPUS:85022230686

SN - 9783319609638

T3 - Communications in Computer and Information Science

SP - 27

EP - 37

BT - Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings

A2 - Gonzalez-Castro, Victor

A2 - Valdes Hernandez, Maria

PB - Springer-Verlag

T2 - 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017

Y2 - 11 July 2017 through 13 July 2017

ER -