Signal, Image and Video Processing

, Volume 12, Issue 2, pp 263–270 | Cite as

Retinal fundus vasculature multilevel segmentation using whale optimization algorithm

  • Gehad HassanEmail author
  • Aboul Ella Hassanien
Original Paper


The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multilevel thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the \(n{-}1\) optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.


Whale optimization algorithm Vessel segmentation Swarm optimization Multilevel segmentation Threshold 


  1. 1.
    Ahmed, M.I., Amin, M.A.: High speed detection of optical disc in retinal fundus image. Signal Image Video Process. 9(1), 77–85 (2015)Google Scholar
  2. 2.
    Binkley, K.J., Hagiwara, M.: Balancing exploitation and exploration in particle swarm optimization: velocity-based reinitialization. Trans. Jpn. Soc. Artif. Intell. 23(1), 27–35 (2008)CrossRefGoogle Scholar
  3. 3.
    Bose, A., Mali, K.: Fuzzy-based artificial bee colony optimization for gray image segmentation. Signal Image Video Process. 10(6), 1–8 (2016)CrossRefGoogle Scholar
  4. 4.
    Cortinovis, D., Srl, O.: Retina blood vessel segmentation with a convolution neural network (u-net). (2016). Online; Accessed 22 Mar 2017
  5. 5.
    Fraz, M.M., Barman, S.A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G.: An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Programs Biomed. 108(2), 600–616 (2012)CrossRefGoogle Scholar
  6. 6.
    Hassan, G., El-Bendary, N., Hassanien, A.E., Fahmy, A.: Blood vessel segmentation approach for extracting the vasculature on retinal fundus images using particle swarm optimization. In: 2015 11th international computer engineering conference (ICENCO), pp. 290–296. IEEE (2015)Google Scholar
  7. 7.
    Hassan, G., El-Bendary, N., Hassanien, A.E., Fahmy, A., Snasel, V., Shoeb, A.: Retinal blood vessel segmentation approach based on mathematical morphology. Procedia Comput. Sci. 65, 612–622 (2015)CrossRefGoogle Scholar
  8. 8.
    Kulkarni, R.V., Venayagamoorthy, G.K.: Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(6), 663–675 (2010)CrossRefGoogle Scholar
  9. 9.
    Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)CrossRefGoogle Scholar
  10. 10.
    Melinščak, M., Prentašić, P., Lončarić, S.: Retinal vessel segmentation using deep neural networks. In: VISAPP 2015 (10th international conference on computer vision theory and applications), pp. 755–582 (2015)Google Scholar
  11. 11.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  12. 12.
    Nazari, P., Pourghassem, H.: An automated vessel segmentation algorithm in retinal images using 2D Gabor wavelet. In: 8th Iranian conference on machine vision and image processing (MVIP), 2013 pp. 145–149. IEEE (2013)Google Scholar
  13. 13.
    Niemeijer, J.J., Staal, B.V., Ginneken, M., Loog, M.D., Abramoff, M.D.: DRIVE: Digital Retinal Images for Vessel Extraction. (2004). Online; Accessed 6 Jan 2015
  14. 14.
    Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J. Biomed. Health Inf. 19(3), 1118–1128 (2015)Google Scholar
  15. 15.
    Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Iterative vessel segmentation of fundus images. IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015)CrossRefGoogle Scholar
  16. 16.
    Tsai, D.M.: A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognit. Lett. 16(6), 653–666 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Faculty of Computers and InformationFayoum UniversityFaiyumEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityGizaEgypt

Personalised recommendations