Retinal blood vessels extraction of challenging images

Toufique Ahmed Soomro; Junbin Gao; Zheng Lihong; Ahmed J. Afifi; Shafiullah Soomro; Manoranjan Paul

doi:10.1007/978-981-13-6661-1_27

Retinal blood vessels extraction of challenging images

Toufique Ahmed Soomro, Junbin Gao, Zheng Lihong, Ahmed J. Afifi, Shafiullah Soomro, Manoranjan Paul

Research output: Book chapter/Published conference paper › Conference paper › peer-review

11 Citations (Scopus)

Abstract

Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.

Original language	English
Title of host publication	Data mining
Subtitle of host publication	16th Australasian conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, revised selected papers
Editors	Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Yun Sing Koh, Rafiqul Islam, Zahidul Islam
Publisher	Springer-Verlag London Ltd.
Pages	347-359
Number of pages	13
ISBN (Electronic)	9789811366611
ISBN (Print)	9789811366604
DOIs	https://doi.org/10.1007/978-981-13-6661-1_27
Publication status	Published - 16 Feb 2019
Event	The 16th Australasian Data Mining Conference - Charles Sturt University , Bathurst, Australia Duration: 28 Nov 2018 → 30 Nov 2018 https://web.archive.org/web/20181122224709/https://ausdm18.ausdm.org/ (Conference website) https://web.archive.org/web/20181202114109/http://ausdm18.ausdm.org/call-for-papers (Call for papers) https://web.archive.org/web/20181211153702/http://ausdm18.ausdm.org/program (Conference program)

Publication series

Name	Communications in Computer and Information Science
Volume	996
ISSN (Print)	1865-0929

Conference

Conference	The 16th Australasian Data Mining Conference
Country/Territory	Australia
City	Bathurst
Period	28/11/18 → 30/11/18
Other	The Australasian Data Mining Conference (AusDM) has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.
Internet address	https://web.archive.org/web/20181122224709/https://ausdm18.ausdm.org/ (Conference website) https://web.archive.org/web/20181202114109/http://ausdm18.ausdm.org/call-for-papers (Call for papers) https://web.archive.org/web/20181211153702/http://ausdm18.ausdm.org/program (Conference program)

UN SDGs

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

Access to Document

10.1007/978-981-13-6661-1_27

Cite this

Soomro, T. A., Gao, J., Lihong, Z., Afifi, A. J., Soomro, S., & Paul, M. (2019). Retinal blood vessels extraction of challenging images. In Y. Zhao, G. Warwick, D. Stirling, C-T. Li, Y. S. Koh, R. Islam, & Z. Islam (Eds.), Data mining: 16th Australasian conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, revised selected papers (pp. 347-359). (Communications in Computer and Information Science; Vol. 996). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-981-13-6661-1_27

Soomro, Toufique Ahmed ; Gao, Junbin ; Lihong, Zheng et al. / Retinal blood vessels extraction of challenging images. Data mining: 16th Australasian conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, revised selected papers. editor / Yanchang Zhao ; Graco Warwick ; David Stirling ; Chang-Tsun Li ; Yun Sing Koh ; Rafiqul Islam ; Zahidul Islam. Springer-Verlag London Ltd., 2019. pp. 347-359 (Communications in Computer and Information Science).

@inproceedings{d146833ddb044614bd866b34c63b636a,

title = "Retinal blood vessels extraction of challenging images",

abstract = "Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.",

keywords = "Enhancement method, Retinal color fundus images, Segmentation of vessels",

author = "Soomro, {Toufique Ahmed} and Junbin Gao and Zheng Lihong and Afifi, {Ahmed J.} and Shafiullah Soomro and Manoranjan Paul",

note = "Includes bibliographical references.; The 16th Australasian Data Mining Conference ; Conference date: 28-11-2018 Through 30-11-2018",

year = "2019",

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doi = "10.1007/978-981-13-6661-1_27",

language = "English",

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editor = "Yanchang Zhao and Graco Warwick and David Stirling and Chang-Tsun Li and Koh, {Yun Sing} and Rafiqul Islam and Zahidul Islam",

booktitle = "Data mining",

address = "Germany",

url = "https://web.archive.org/web/20181122224709/https://ausdm18.ausdm.org/, https://web.archive.org/web/20181202114109/http://ausdm18.ausdm.org/call-for-papers, https://web.archive.org/web/20181211153702/http://ausdm18.ausdm.org/program",

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Soomro, TA, Gao, J, Lihong, Z, Afifi, AJ, Soomro, S & Paul, M 2019, Retinal blood vessels extraction of challenging images. in Y Zhao, G Warwick, D Stirling, C-T Li, YS Koh, R Islam & Z Islam (eds), Data mining: 16th Australasian conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, revised selected papers. Communications in Computer and Information Science, vol. 996, Springer-Verlag London Ltd., pp. 347-359, The 16th Australasian Data Mining Conference, Bathurst, New South Wales, Australia, 28/11/18. https://doi.org/10.1007/978-981-13-6661-1_27

Retinal blood vessels extraction of challenging images. / Soomro, Toufique Ahmed; Gao, Junbin; Lihong, Zheng et al.
Data mining: 16th Australasian conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, revised selected papers. ed. / Yanchang Zhao; Graco Warwick; David Stirling; Chang-Tsun Li; Yun Sing Koh; Rafiqul Islam; Zahidul Islam. Springer-Verlag London Ltd., 2019. p. 347-359 (Communications in Computer and Information Science; Vol. 996).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Retinal blood vessels extraction of challenging images

AU - Soomro, Toufique Ahmed

AU - Gao, Junbin

AU - Lihong, Zheng

AU - Afifi, Ahmed J.

AU - Soomro, Shafiullah

AU - Paul, Manoranjan

N1 - Includes bibliographical references.

PY - 2019/2/16

Y1 - 2019/2/16

N2 - Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.

AB - Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.

KW - Enhancement method

KW - Retinal color fundus images

KW - Segmentation of vessels

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BT - Data mining

A2 - Zhao, Yanchang

A2 - Warwick, Graco

A2 - Stirling, David

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Soomro TA, Gao J, Lihong Z, Afifi AJ, Soomro S, Paul M. Retinal blood vessels extraction of challenging images. In Zhao Y, Warwick G, Stirling D, Li C-T, Koh YS, Islam R, Islam Z, editors, Data mining: 16th Australasian conference, AusDM 2018, Bahrurst, NSW, Australia, November 28–30, 2018, revised selected papers. Springer-Verlag London Ltd. 2019. p. 347-359. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-6661-1_27

Retinal blood vessels extraction of challenging images

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