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

5 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 - 16th Australasian Conference, AusDM 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 (Print)	9789811366604
DOIs	https://doi.org/10.1007/978-981-13-6661-1_27
Publication status	Published - 16 Feb 2019
Event	16th Australasian Conference on Data Mining, AusDM 2018 - Charles Sturt University , Bathurst, Australia Duration: 28 Nov 2018 → 30 Nov 2018 https://ausdm18.ausdm.org/

Publication series

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

Conference

Conference	16th Australasian Conference on Data Mining, AusDM 2018
Country	Australia
City	Bathurst
Period	28/11/18 → 30/11/18
Internet address	https://ausdm18.ausdm.org/

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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

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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, 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 ; Afifi, Ahmed J. ; Soomro, Shafiullah ; Paul, Manoranjan. / Retinal blood vessels extraction of challenging images. Data Mining - 16th Australasian Conference, AusDM 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).

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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",

year = "2019",

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

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booktitle = "Data Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers",

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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, Revised Selected Papers. Communications in Computer and Information Science, vol. 996, Springer-Verlag London Ltd., pp. 347-359, 16th Australasian Conference on Data Mining, AusDM 2018, Bathurst, 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; Afifi, Ahmed J.; Soomro, Shafiullah; Paul, Manoranjan.

Data Mining - 16th Australasian Conference, AusDM 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

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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

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

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KW - Segmentation of vessels

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A2 - Stirling, David

A2 - Li, Chang-Tsun

A2 - Koh, Yun Sing

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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, Revised Selected Papers. Springer-Verlag London Ltd. 2019. p. 347-359. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-13-6661-1_27

Retinal blood vessels extraction of challenging images

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