Automated artery-vein classification in fundus color images

Yi Yang; Wei Bu; Kuanquan Wang; Yalin Zheng; Xiangqian Wu

doi:10.1007/978-981-10-2053-7_21

Automated artery-vein classification in fundus color images

Yi Yang
, Wei Bu
, Kuanquan Wang
, Yalin Zheng
, Xiangqian Wu^*

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Harbin Institute of Technology
University of Liverpool

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The estimation of Arterio-Venous ratio (AVR) is an important phase in diagnosing various vascular diseases e.g. Diabetic Retinopathy. For calculating this value, it is essential to differentiate the vessels into arteries and veins. This paper presents a novel structural and automated method for artery/vein vessels classification in retinal images. Our method is tested on DRIVE database and the classification accuracy is 88.7% for pixels and 89.07% for vessel lines, respectively, which demonstrate the effectives of our approach. Our method will help to achieve the fundus disease surveillance on mobile and remote medical treatment. It has a remarkable social significance.

Original language	English
Title of host publication	Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings
Editors	Wanxiang Che, Hongzhi Wang, Shaoliang Peng, Weipeng Jing, Guanglu Sun, Xianhua Song, Zeguang Lu, Qilong Han, Junyu Lin, Hongtao Song
Publisher	Springer Verlag
Pages	228-237
Number of pages	10
ISBN (Print)	9789811020520
DOIs	https://doi.org/10.1007/978-981-10-2053-7_21
State	Published - 2016
Externally published	Yes
Event	2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016 - Harbin, China Duration: 20 Aug 2016 → 22 Aug 2016

Publication series

Name	Communications in Computer and Information Science
Volume	623
ISSN (Print)	1865-0929

Conference

Conference	2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016
Country/Territory	China
City	Harbin
Period	20/08/16 → 22/08/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Adaptive histogram equalization
Arteries and veins
Diabetic Retinopathy
Feature extraction
Support vector machines
Vessel classification

Access to Document

10.1007/978-981-10-2053-7_21

Cite this

Yang, Y., Bu, W., Wang, K., Zheng, Y., & Wu, X. (2016). Automated artery-vein classification in fundus color images. In W. Che, H. Wang, S. Peng, W. Jing, G. Sun, X. Song, Z. Lu, Q. Han, J. Lin, & H. Song (Eds.), Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings (pp. 228-237). (Communications in Computer and Information Science; Vol. 623). Springer Verlag. https://doi.org/10.1007/978-981-10-2053-7_21

Yang, Yi ; Bu, Wei ; Wang, Kuanquan et al. / Automated artery-vein classification in fundus color images. Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. editor / Wanxiang Che ; Hongzhi Wang ; Shaoliang Peng ; Weipeng Jing ; Guanglu Sun ; Xianhua Song ; Zeguang Lu ; Qilong Han ; Junyu Lin ; Hongtao Song. Springer Verlag, 2016. pp. 228-237 (Communications in Computer and Information Science).

@inproceedings{4c4940f693df441db92d83d8a7f6e833,

title = "Automated artery-vein classification in fundus color images",

abstract = "The estimation of Arterio-Venous ratio (AVR) is an important phase in diagnosing various vascular diseases e.g. Diabetic Retinopathy. For calculating this value, it is essential to differentiate the vessels into arteries and veins. This paper presents a novel structural and automated method for artery/vein vessels classification in retinal images. Our method is tested on DRIVE database and the classification accuracy is 88.7\% for pixels and 89.07\% for vessel lines, respectively, which demonstrate the effectives of our approach. Our method will help to achieve the fundus disease surveillance on mobile and remote medical treatment. It has a remarkable social significance.",

keywords = "Adaptive histogram equalization, Arteries and veins, Diabetic Retinopathy, Feature extraction, Support vector machines, Vessel classification",

author = "Yi Yang and Wei Bu and Kuanquan Wang and Yalin Zheng and Xiangqian Wu",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016 ; Conference date: 20-08-2016 Through 22-08-2016",

year = "2016",

doi = "10.1007/978-981-10-2053-7\_21",

language = "英语",

isbn = "9789811020520",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "228--237",

editor = "Wanxiang Che and Hongzhi Wang and Shaoliang Peng and Weipeng Jing and Guanglu Sun and Xianhua Song and Zeguang Lu and Qilong Han and Junyu Lin and Hongtao Song",

booktitle = "Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings",

address = "德国",

}

Yang, Y, Bu, W, Wang, K, Zheng, Y & Wu, X 2016, Automated artery-vein classification in fundus color images. in W Che, H Wang, S Peng, W Jing, G Sun, X Song, Z Lu, Q Han, J Lin & H Song (eds), Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. Communications in Computer and Information Science, vol. 623, Springer Verlag, pp. 228-237, 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Harbin, China, 20/08/16. https://doi.org/10.1007/978-981-10-2053-7_21

Automated artery-vein classification in fundus color images. / Yang, Yi; Bu, Wei; Wang, Kuanquan et al.
Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. ed. / Wanxiang Che; Hongzhi Wang; Shaoliang Peng; Weipeng Jing; Guanglu Sun; Xianhua Song; Zeguang Lu; Qilong Han; Junyu Lin; Hongtao Song. Springer Verlag, 2016. p. 228-237 (Communications in Computer and Information Science; Vol. 623).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automated artery-vein classification in fundus color images

AU - Yang, Yi

AU - Bu, Wei

AU - Wang, Kuanquan

AU - Zheng, Yalin

AU - Wu, Xiangqian

N1 - Publisher Copyright: © Springer Science+Business Media Singapore 2016.

PY - 2016

Y1 - 2016

N2 - The estimation of Arterio-Venous ratio (AVR) is an important phase in diagnosing various vascular diseases e.g. Diabetic Retinopathy. For calculating this value, it is essential to differentiate the vessels into arteries and veins. This paper presents a novel structural and automated method for artery/vein vessels classification in retinal images. Our method is tested on DRIVE database and the classification accuracy is 88.7% for pixels and 89.07% for vessel lines, respectively, which demonstrate the effectives of our approach. Our method will help to achieve the fundus disease surveillance on mobile and remote medical treatment. It has a remarkable social significance.

AB - The estimation of Arterio-Venous ratio (AVR) is an important phase in diagnosing various vascular diseases e.g. Diabetic Retinopathy. For calculating this value, it is essential to differentiate the vessels into arteries and veins. This paper presents a novel structural and automated method for artery/vein vessels classification in retinal images. Our method is tested on DRIVE database and the classification accuracy is 88.7% for pixels and 89.07% for vessel lines, respectively, which demonstrate the effectives of our approach. Our method will help to achieve the fundus disease surveillance on mobile and remote medical treatment. It has a remarkable social significance.

KW - Adaptive histogram equalization

KW - Arteries and veins

KW - Diabetic Retinopathy

KW - Feature extraction

KW - Support vector machines

KW - Vessel classification

UR - https://www.scopus.com/pages/publications/84981276355

U2 - 10.1007/978-981-10-2053-7_21

DO - 10.1007/978-981-10-2053-7_21

M3 - 会议稿件

AN - SCOPUS:84981276355

SN - 9789811020520

T3 - Communications in Computer and Information Science

SP - 228

EP - 237

BT - Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings

A2 - Che, Wanxiang

A2 - Wang, Hongzhi

A2 - Peng, Shaoliang

A2 - Jing, Weipeng

A2 - Sun, Guanglu

A2 - Song, Xianhua

A2 - Lu, Zeguang

A2 - Han, Qilong

A2 - Lin, Junyu

A2 - Song, Hongtao

PB - Springer Verlag

T2 - 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

Y2 - 20 August 2016 through 22 August 2016

ER -

Yang Y, Bu W, Wang K, Zheng Y, Wu X. Automated artery-vein classification in fundus color images. In Che W, Wang H, Peng S, Jing W, Sun G, Song X, Lu Z, Han Q, Lin J, Song H, editors, Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings. Springer Verlag. 2016. p. 228-237. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-2053-7_21

Automated artery-vein classification in fundus color images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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