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Australasian Conference on Data Mining

AusDM 2018: Data Mining pp 347–359Cite as

Retinal Blood Vessels Extraction of Challenging Images

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 996))

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.

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Toufique Ahmed Soomro, Zheng Lihong & Manoranjan Paul

  2. The Business School, The University of Sydney, Sydney, Australia

    Junbin Gao

  3. Computer Vision and Remote Sensing, Technische Universität Berlin, Berlin, Germany

    Ahmed J. Afifi

  4. Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea

    Shafiullah Soomro

Authors
  1. Toufique Ahmed Soomro
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  2. Junbin Gao
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  3. Zheng Lihong
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  4. Ahmed J. Afifi
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  5. Shafiullah Soomro
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  6. Manoranjan Paul
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Corresponding author

Correspondence to Toufique Ahmed Soomro .

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  2. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. Data Science and Engineering, Australian Taxation Office, Canberra, Australia

    Graco Warwick

  5. Department of Information Technology, University of Wollongong, Wollongong, NSW, Australia

    David Stirling

  6. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Chang-Tsun Li

  7. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Zahidul Islam

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Soomro, T.A., Gao, J., Lihong, Z., Afifi, A.J., Soomro, S., Paul, M. (2019). Retinal Blood Vessels Extraction of Challenging Images. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_27

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  • DOI: https://doi.org/10.1007/978-981-13-6661-1_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6660-4

  • Online ISBN: 978-981-13-6661-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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