Skip to main content
SpringerLink
Log in

Automatic Blood Vessel Segmentation in Retinal Fundus Images Using Image Enhancement and Dynamic Gray-Level Thresholding

  • Conference paper
  • First Online:
Proceedings of International Conference on Computational Intelligence and Data Engineering

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 99))

Abstract

Blood vessel is one of the most important retinal regions used for identification of retinal diseases like glaucoma, diabetic retinopathy and occlusion through blood vessel features. This paper presented an automated image processing techniques for blood vessel segmentation through image enhancement and dynamic gray-level thresholding. Proposed approach contains of six processes: color channel selection, image complement, image enhancement, optic disk removal, dynamic gray-level thresholding and binarization. Initially, green color channel is selected from original RGB color fundus images for clear visibility of blood vessels followed by image complement techniques to differentiate blood vessel features with other retinal features. Then, complemented image is further enhanced to improve the visibility of blood vessels including thin vessels for accurate extraction, and then, disk structuring element is applied for optic disk removal. Finally, blood vessel is segmented by applying image binarization using dynamic gray-level thresholding. Proposed approach achieved good results in terms of accuracy and specificity of 95.51 and 99.14% on DRIVE dataset and 95.67 and 98.33% on HRF dataset. Also, experimental results were compared with the state-of-the-art methods and represent our proposed method achieved high accuracy and very much helpful for the ophthalmologist for disease identification during earlier stage.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jebaseeli TJ, Deva Durai CA, Peter JD (2019) Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM. Optik 199(March 2018):163328

    Google Scholar 

  2. Câmara Neto L, Ramalho GLB, Rocha Neto JFS, Veras RMS, Medeiros FNS (2017) An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images. Expert Syst Appl 78:182–192

    Google Scholar 

  3. Vostatek P, Claridge E, Uusitalo H, Hauta-Kasari M, Fält P, Lensu L (2017) Performance comparison of publicly available retinal blood vessel segmentation methods. Comput Med Imaging Graph 55:2–12

    Article  Google Scholar 

  4. Jyotiprava Dash NB (2017) A method for blood vessel segmentation in retinal images using morphological reconstruction. IEEE xplore

    Google Scholar 

  5. Wankhede PR, Khanchandani KB (2020) Feature extraction in retinal images using automated methods. Int J Sci Technol Res 9(3):5326–5333

    Google Scholar 

  6. Imran A, Li J, Pei Y, Yang J-J, Wang Q (2019) Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access 7:114862–114887

    Article  Google Scholar 

  7. Moccia S, De Momi E, El S, Mattos LS (2018) Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics. Comput Meth Prog Biomed 158:71–91

    Article  Google Scholar 

  8. Sharma A, Rani S (2016) An automatic segmentation and detection of blood vessels and optic disc in retinal images. In: International conference on communication and signal processing, ICCSP 2016, pp 1674–1678

    Google Scholar 

  9. Mano A. Contrast enhanced superpixel based segmentation of retinal images

    Google Scholar 

  10. Shaik M (2016) Blood vessel segmentation in retinal images using multiscale image enhancement. Local Entropy Thresh Cluster 1(1):38–43

    Google Scholar 

  11. Biswal B, Pooja T, Subrahmanyam NB (2018) Robust retinal blood vessel segmentation using hybrid active contour model. IET Image Proc 12(3):389–399

    Article  Google Scholar 

  12. Borah KJ, Borbhuyan AA, Rahman SS, Baruah A (2016) Wavelet based blood vessel segmentation in retinal image analysis. Int J Eng Res Technol V5(11):324–327

    Google Scholar 

  13. Ramani RG, Shanthamalar JJ (2020) Improved image processing techniques for optic disc segmentation in retinal fundus images. Biomed Sig Process Control 58:101832

    Google Scholar 

  14. Wang X, Jiang X, Ren J (2019) Blood vessel segmentation from fundus image by a cascade classification framework. Pattern Recogn 88:331–341

    Article  Google Scholar 

  15. Samuel PM, Veeramalai T (2019) Multilevel and multiscale deep neural network for retinal blood vessel segmentation. Symmetry 11(7)

    Google Scholar 

  16. Bandara AMRR, Giragama PWGRMPB (2018) A retinal image enhancement technique for blood vessel segmentation algorithm. In: 2017 IEEE international conference on industrial and information systems, ICIIS 2017—Proceedings, vol 2018, Janua, pp 1–5

    Google Scholar 

  17. Sabaz F, Atila U (2017) ROI detection and vessel segmentation in retinal image. Int Arch Photogram Remote Sens Spatial Inf Sci—ISPRS Arch 42(4W6):85–89

    Google Scholar 

  18. Kasmin F, Abdullah A, Prabuwono AS (2017) Retinal blood vessel segmentation using ensemble of single oriented mask filters. Int J Electr Comp Eng 7(3):1414–1422

    Google Scholar 

  19. Xue LY, Lin JW, Cao XR, Yu L (2018) Retinal blood vessel segmentation using saliency detection model and region optimization. J Algo Comput Technol 12(1):3–12

    Article  MathSciNet  Google Scholar 

  20. Curran K, Souahlia A, Belatreche A, Benyettou A (2017) Blood vessel segmentation in retinal images using echo state networks. In: 2017 Ninth international conference on advanced computational intelligence (ICACI), pp 1–8

    Google Scholar 

  21. Ali A, Zaki WMDW, Hussain A (2019) Retinal blood vessel segmentation from retinal image using B-COSFIRE and adaptive thresholding. Indo J Electr Eng Comp Sci 13(3):1199–1207

    Google Scholar 

  22. Oliveira WS, Teixeira JV, Ren TI, Cavalcanti GDC, Sijbers J (2016) Unsupervised retinal vessel segmentation using combined filters. PLoS ONE 11(2):1–21

    Google Scholar 

  23. Ganesan K, Naik G, Adapa D, Raj ANJ, Alisetti SN, Zhuang Z (2020) A supervised blood vessel segmentation technique for digital fundus images using Zernike moment based features. PLoS ONE 15(3):1–23

    Google Scholar 

  24. Memari N, Ramli AR, Bin Saripan MI, Mashohor S, Moghbel M (2019) Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment. J Med Biol Eng 39(5):713–731

    Google Scholar 

  25. Christodoulidis A, Hurtut T, Ben Tahar H, Cheriet F (2016) A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images. Comput Med Imag Graphics 52:28–43

    Google Scholar 

  26. Shukla AK, Pandey RK, Pachori RB (2020) A fractional filter based efficient algorithm for retinal blood vessel segmentation. Biomed Sig Process Control 59(1–16):101883

    Google Scholar 

  27. Cheng Y, Ma M, Zhang L, Jin CJ, Ma Li, Zhou Y (2020) Retinal blood vessel segmentation based on densely connected U-Net. Math Biosci Eng 17:3088–3108

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Centre For Research, Anna University, under the Anna Centenary Research Fellowship, Anna University, Chennai, India (Reference: CFR/ACRF/2018/AR1).

Author information

Authors and Affiliations

  1. Department of Information Science and Technology, Anna University, Chennai, India

    J. Jeslin Shanthamalar & R. Geetha Ramani

Authors
  1. J. Jeslin Shanthamalar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Geetha Ramani
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Calcutta, Kolkata, India

    Nabendu Chaki

  2. VIT-AP University, Amaravati, India

    Nagaraju Devarakonda

  3. Ca’ Foscari University, Venice, Italy

    Agostino Cortesi

  4. VIT-AP University, Amaravati, India

    Hari Seetha

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shanthamalar, J.J., Ramani, R.G. (2022). Automatic Blood Vessel Segmentation in Retinal Fundus Images Using Image Enhancement and Dynamic Gray-Level Thresholding. In: Chaki, N., Devarakonda, N., Cortesi, A., Seetha, H. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 99. Springer, Singapore. https://doi.org/10.1007/978-981-16-7182-1_27

Download citation

Publish with us

Policies and ethics

Search

Navigation