Advertisement

Advances in Data Analysis and Classification

, Volume 11, Issue 3, pp 611–627 | Cite as

Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search

  • E. Emary
  • Hossam M. Zawbaa
  • Aboul Ella Hassanien
  • B. Parv
Regular Article

Abstract

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

Keywords

Flower pollination search algorithm Pattern search Multi-objective retinal vessel localization Bio-inspired optimization Evolutionary computation 

Mathematics Subject Classification

68T20 

References

  1. Bezdek JC (1981) Pattern Recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRefzbMATHGoogle Scholar
  2. Delibasis KK, Kechriniotis AI, Tsonos C, Assimakis N (2010) Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput Methods Prog Biomed 100:108–122CrossRefGoogle Scholar
  3. Dembele D (2008) Multi-objective optimization for clustering 3-way gene expression data. Adv Data Anal Classif 2(3):211–225MathSciNetCrossRefzbMATHGoogle Scholar
  4. Emary E, Zawbaa HM, Hassanien AE, Schaefer G, Azar AT (2014) Retinal blood vessel segmentation using bee colony optimisation and pattern search. International joint conference on neural networks (IJCNN), ChinaGoogle Scholar
  5. Emary E, Zawbaa HM, Hassanien AE, Schaefer G, Azar AT (2014) Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimized with cuckoo search. Proc. IEEE international joint conference on neural networks (IJCNN), ChinaGoogle Scholar
  6. Emary E, Zawbaa HM, Hassanien AE, Tolba MF, Snasel V (2014) Retinal vessel segmentation based on flower pollination search algorithm. International conference on innovations in bio-inspired computing and applications (IBICA), Czech Republic, pp 1001–1006Google Scholar
  7. Foracchia M, Grisan E, Ruggeri A (2011) Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images. In: Book abstracts of 2nd international workshop on computer assisted fundus image analysisGoogle Scholar
  8. Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka AR, Owen CG (2012a) An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Prog Biomed 108(2):600–616Google Scholar
  9. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012b) Blood vessel segmentation methodologies in retinal images-a survey. Comput Methods Prog Biomed 108(1):407–433Google Scholar
  10. Hassanien AE, Emary E, Zawbaa HM (2015) Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search. Vis Commun Image Represent Elsevier 31:186–196CrossRefGoogle Scholar
  11. Hooshyar S, Khayati R (2010) Retina vessel detection using fuzzy ant colony algorithm. Canadian Conference Computer and Robot Vision (CRV), Ottawa, pp 239–244Google Scholar
  12. Kose C, Kibas CI (2011) A personal identification system using retinal vasculature in retinal fundus images. Expert Syst Appl 38:13670–13681Google Scholar
  13. Lewis RM, Torczon V (1996) Rank ordering and positive basis in pattern search algorithms. ICASE NASA Langley Research Center TRGoogle Scholar
  14. Liu Z, Zhao X, Zuo MJ, Xu H (2014) Feature selection for fault level diagnosis of planetary gearboxes. Adv Data Anal Classif 8(4):377–401MathSciNetCrossRefGoogle Scholar
  15. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, University of California Press, pp 281–297Google Scholar
  16. Marin D, Aquino A, Gegundez-Arias ME, Bravo JM (2011) A new supervised method for blood vessel segmentation in retinal images by using grey-level and moment invariants-based features. IEEE Trans Med Imaging 30(1):146–158CrossRefGoogle Scholar
  17. Niemeijer M, Staal JJ, van Ginneken B, Loog M, Abramoff MD (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE Med Imaging 5370:648–656Google Scholar
  18. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530CrossRefGoogle Scholar
  19. Pavlyukevich I (2007) Levy flights, non-local search and simulated annealing. Comput Phys 226:1830–1844MathSciNetCrossRefzbMATHGoogle Scholar
  20. Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, Ginneken BV (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509CrossRefGoogle Scholar
  21. Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optim 7(1):1–25MathSciNetCrossRefzbMATHGoogle Scholar
  22. Vijayakumari V, Suriyanarayanan N (2012) Survey on the detection methods of blood vessel in retinal images. Eur J Sci Res 68(1):83–92Google Scholar
  23. Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708–717CrossRefGoogle Scholar
  24. Wang G, Wang Z, Chen Y, Zhao W (2015) Robust point matching method for multimodal retinal image registration. Biomed Signal Process Control 19:68–76CrossRefGoogle Scholar
  25. Wangc Y, Ji G, Lin P, Trucco E (2013) Retinal vessel segmentation using multiwavelet kernels and multiscalehierarchical decomposition. Pattern Recogn 46:2117–2133CrossRefGoogle Scholar
  26. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83CrossRefGoogle Scholar
  27. Yang XS (2012) Flower pollination algorithm for global optimization. Unconv Comput Nat Comput Lect Notes Comput Sci 7445:240–249zbMATHGoogle Scholar
  28. Yang XS, Karamanoglu M, He X (2013) Multi-objective flower algorithm for optimization. Proc Comput Sci 18:861–868CrossRefGoogle Scholar
  29. Zhang B, Zhang L, Zhangb L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40:438–445CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • E. Emary
    • 1
    • 2
  • Hossam M. Zawbaa
    • 3
    • 4
  • Aboul Ella Hassanien
    • 1
    • 2
  • B. Parv
    • 4
  1. 1.Faculty of Computers and InformationCairo UniversityGizaEgypt
  2. 2.Scientific Research Group in Egypt (SRGE)CairoEgypt
  3. 3.Faculty of Computers and InformationBeni-Suef UniversityBeni SuefEgypt
  4. 4.Faculty of Mathematics and Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania

Personalised recommendations