Skip to main content
SpringerLink
Log in

Retinal Blood Vessels Segmentation Based on Bio-Inspired Algorithm

  • Chapter
  • First Online:
Applications of Intelligent Optimization in Biology and Medicine

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 96))

Abstract

The diabetic retinopathy disease spreads diabetes on the retina vessels thus they lose blood supply that causes blindness in short time, so early detection of diabetes prevents blindness in more than 50 % of cases. The early detection can be achieved by automated segmentation of blood vessels in retinal images which is two-class classification problem; vessel-like or non-vessel. This chapter presents an ant colony system based approach and its improvements for the segmentation of retinal blood vessels. To minimize classification complexity, time and maximizes its accuracy, features selection is an essential step for reducing data dimensionality by removing redundant features. The performance of the presented approach on the benchmark databases of retinal images is considerable and promising as it uses features that are simple, fast in computation and needn’t to be computed at multiple scales or orientations.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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. Aha, D.W., Bankert, R.: A comparative evaluation of sequential feature selection algorithms. In: Proceedings of 5th International Workshop on Artificial Intelligence and Statistics, vol 17 (1995)

    Google Scholar 

  2. Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 28, 14881497 (2009)

    Article  Google Scholar 

  3. Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 37, 262267 (2007)

    Article  Google Scholar 

  4. Asad, A.H., Azar, A.T., El-Bendary, N., Hassanien, A.E.: Ant colony based feature selection heuristics for retinal vessel segmentation. In: Proceedings of 2nd International Symposium on Intelligent Informatics (ISI13), Mysore, India, 22–25 Aug 2013

    Google Scholar 

  5. Asad, A.H., Azar, A.T., Hassanien, A.E.: Ant colony-based system for retinal blood vessels segmentation. In: Seventh International Conference on Bio-Inspired Computing: Theories and Application (BIC-TA 2012), Gwalior, India, 14–16 Dec 2012

    Google Scholar 

  6. Asad, A.H., Azar, A.T., Mostafa Fouad, M.M., Hassanien, A.E.: An improved ant colony system for retinal blood vessel segmentation. In: Proceedings of the 2013 IEEE Federated Conference on Computer Science and Information Systems (FedCSIS), Karcow, Poland, pp. 199–205, 8–11 Sept 2013

    Google Scholar 

  7. Asad, A.H., El-Amry, E., Hassanien, A.E., Tolba, M.: New global update mechanism of ant colony system for retinal vessel segmentation. In: Proceedings of the 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13), Tunisia, pp. 222–228, 4–6 Dec (2013)

    Google Scholar 

  8. Asad, A.H., Azar, A.T., Hassanien, A.E.: Integrated features based on gray-level and Hu moment invariants with ant colony system for retinal blood vessels segmentation. Int. J. Syst. Boil. Biomed. Tech. 1(4), 61–74 (2012)

    Google Scholar 

  9. Asad, A.H., Azar, A.T., Hassanien, A.E.: A comparative study on feature selection for retinal vessel segmentation using ant colony system. Adv. Intell. Syst. Comput. 235, 1–11 (2013). doi:10.1007/978-3-319-01778-51

    Article  Google Scholar 

  10. Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)

    Google Scholar 

  11. Barrett, W.A., Mortensen, E.N.: Interactive live-wire boundary extraction. Med. Image Anal. 1, 331341 (1997)

    Article  Google Scholar 

  12. Beyer, H.G., Schwefel, H.P.: Evolution strategies. Nat. Comput. 1, 352 (2002)

    Article  MathSciNet  Google Scholar 

  13. Binitha, S., Siva Sathya, S.: A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2) (2012)

    Google Scholar 

  14. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. Oxford University Press, Oxford (1999)

    Google Scholar 

  15. CHASE-DB1 (2011). http://sec.kingston.ac.uk/retinal

  16. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8(3), 263269 (1989)

    Article  Google Scholar 

  17. Cinsdikici, M.J., Aydn, D.: Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput. Methods Programs Biomed. 96(2), 85–95 (2009)

    Article  Google Scholar 

  18. Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Berlin (1999)

    Google Scholar 

  19. Delibasis, K.K., Kechriniotis, A.I., Tsonos, C., Assimakis, N.: Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput. Methods Programs Biomed. 100, 108122 (2010)

    Article  Google Scholar 

  20. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man. Cybern. Part B 26, 2941 (1996)

    Google Scholar 

  21. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 5366 (1997)

    Article  Google Scholar 

  22. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    Google Scholar 

  23. Espona, L., Carreira, M.J., Penedo, M.G., Ortega, M.: Retinal vessel tree segmentation using a deformable contour model. In: Proceedings of 19th International Conference on Pattern Recognition (ICPR), 14 (2008)

    Google Scholar 

  24. Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using SFLA (2003)

    Google Scholar 

  25. Foracchia, M., Grisan, E., Ruggeri, A.: Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images. In: Book Abstracts 2nd International Workshop on Computer Assisted Fundus Image Analysis (2011)

    Google Scholar 

  26. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A., William, W., Alan, C., Scott, D.: Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Interventation (MICCAITM98), p. 130. Springer, Berlin/Heidelberg (1998)

    Google Scholar 

  27. 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. 58(5), 1183-1192 (2011)

    Google Scholar 

  28. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images-a survey. Comput. Methods Programs Biomed. 108(1), 33–407 (2012). doi:10.1016/j.cmpb.2012.03.009

    Article  Google Scholar 

  29. Fraz, M.M., Barman, S.A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 14271435 (2012)

    Article  Google Scholar 

  30. Fritzsche, K., Can, A., Shen, H., Tsai, C., Turner, J., Tanenbuam, H.L., Stewart, C.V., Roysam, B., Suri, J.S., Laxminarayan, S.: Automated model based segmentation, tracing and analysis of retinal vasculature from digital fundus images. In: State-of-The-Art Angiography, Applications and Plaque Imaging Using MR, CT Ultrasound and X-rays, pp. 225–298. Academic Press (2003)

    Google Scholar 

  31. Gini, C.: Variabilite e mutabilita. Memorie di metodologia statistica (1912)

    Google Scholar 

  32. Goatman, K., Charnley, A., Webster, L., Nussey, S.: Assessment of automated disease detection in diabetic retinopathy screening using two-field photography. PLoS One 6(12), e27524 (2011)

    Article  Google Scholar 

  33. Grisan, E., Ruggeri, A.: A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In: Proceedings of the 25th IEEE Annual International Conference of Engineering in Medicine and Biology Society, vol. 891, pp. 890–893 (2003)

    Google Scholar 

  34. Haddouche, A., Adel, M., Rasigni, M., Conrath, J., Bourennane, S.: Detection of the foveal a vascular zone on retinal angiograms using Markov random fields. Digit. Signal Process. 20, 149–154 (2010)

    Google Scholar 

  35. Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of 17th International Conference on Machine Learning (ICML), pp. 359–366. San Francisco, CA (2000)

    Google Scholar 

  36. He, S., Wu, Q.H., Saunders, J.R.: A novel group search optimizer inspired by animal behavioural ecology. In: IEEE Congress on Evolutionary Computation, pp. 1272–1278 (2006)

    Google Scholar 

  37. Heneghan, C., Flynn, J., OKeefe, M., Cahill, M.: Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med. Image Anal. 6, 407–429 (2002)

    Google Scholar 

  38. Holland, J.H.: Genetic algorithms and the optimal allocation of trials. SIAM J. Comput. 2(2), 88105 (1973)

    Article  MATH  Google Scholar 

  39. Hooshyar, S., Khayati, R.: Retina vessel detection using fuzzy ant colony algorithm. In: Proceedings of Canadian Conference on Computer and Robot Vision (CRV), pp. 239–244 (2010). doi:10.1109/CRV.2010.38

  40. Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19(3), 203–210 (2000)

    Article  Google Scholar 

  41. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179187 (1962)

    Google Scholar 

  42. Hua, J.P., Tembe, W.D., Dougherty, E.R.: Performance of feature-selection methods in the classification of high-dimension data. Pattern Recogn. 42(3), 409–424 (2009)

    Article  MATH  Google Scholar 

  43. Huiqi, L., Chutatape, O.: Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51, 246–254 (2004)

    Google Scholar 

  44. Jones, S., Edwards, R.T.: Diabetic retinopathy screening: a systematic review of the economic evidence. Diabet Med. 27(3), 249–256 (2010)

    Article  Google Scholar 

  45. Kanski, J.J.: Clinical Ophthalmology, 6th edn. Elsevier Health Sciences, London (2007)

    Google Scholar 

  46. Karaboga, D., Basturk, R.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)

    Google Scholar 

  47. Kelvin, P., Ghassan, H., Rafeef, A.: Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images. In: Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Brisbane, Australia (2007)

    Google Scholar 

  48. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)

    Google Scholar 

  49. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: The Proceedings of 9th International Conference on Machine Learning, Aberdeen, Scotland, Morgan Kaufmann, Los Altos, CA. pp. 249–256 (1992)

    Google Scholar 

  50. Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: Proceedings of the Seventh European Conference on Machine Learning, vol. 784, pp. 171–182. Springer, Berlin (1994)

    Google Scholar 

  51. Koza, John R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    Google Scholar 

  52. Kse, C., Ikibas, C.: A personal identification system using retinal vasculature in retinal fundus images. Expert Syst. Appl. 38, 13670–13681 (2011)

    Google Scholar 

  53. Leung, H., Wang, J.J., Rochtchina, E., Wong, T.Y., Klein, R., Mitchell, P.: Impact of current and past blood pressure on retinal arteriolar diameter in an older population. J. Hypertens. 22(8), 1543–1549 (2004)

    Article  Google Scholar 

  54. Li, X., Shao, Z., Qian, J.: An optimizing method base on autonomous animates: fish- swarm algorithm. Syst. Eng. Theory Pract. 22, 32–38 (2002)

    Google Scholar 

  55. Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Kennedy, R.L.: Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Trans. Med. Imag. 23, 1196–1204 (2000)

    Google Scholar 

  56. Lupascu, C.A., Tegolo, D., Trucco, E.: A comparative study on feature selection for retinal vessel segmentation using FABC. In: Proceedings of 13th International Conference on Computer Analysis of Images and Patterns (CAIP), pp. 655–662 (2009)

    Google Scholar 

  57. Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using adaboost. IEEE Trans. Inf. Technol. Biomed. 14(5), 1267–1274 (2010)

    Google Scholar 

  58. Mahalem, R.A., Chavan, S.D.: A survey: evolutionary and swarm based bio-inspired optimization algorithms. Int. J. Sci. Res. Publ. 2(12) (2012)

    Google Scholar 

  59. Marin, C., Penedo, G., Penas, M., Carreira, J., Gonzalez, F.: Personal authentication using digital retinal images. Pattern Anal. Appl. 9, 21–33 (2006)

    Google Scholar 

  60. Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using grey-level and moment invariants-based features. IEEE Trans. Med. Imag. 30(1), 146–158 (2011)

    Article  Google Scholar 

  61. Mark, F.: Foundations of swarm intelligence: from principles to practice. Swarming: Network Enabled C4ISR c (2003)

    Google Scholar 

  62. Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imag. 25(9), 1200–1213 (2006)

    Google Scholar 

  63. MESSIDOR: Methods for Evaluating Segmentation and Indexing techniques Dedicated to Retinal Ophthalmology. http://messidor.crihan.fr/index-en.php

  64. Miri, M.S., Mahloojifar, A.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans. Biomed. Eng. 58(5), 1183–1192 (2011)

    Google Scholar 

  65. Mitchell, P., Leung, H., Wang, J.J., Rochtchina, E., Lee, A.J., Wong, T.Y., Klein, R.: Retinal vessel diameter and open-angle glaucoma: the blue mountains eye study. Ophthalmol. 112(2), 50–245 (2005)

    Article  Google Scholar 

  66. Morello, C.M.: Etiology and natural history of diabetic retinopathy: an overview. Am. J. Health Syst. Pharm. 64(17 Suppl., 12), S3–S7 (2007)

    Google Scholar 

  67. Niemeijer, M., Staal, J., Ginneken, B.V., Loog, M., Abramoff, M.D., Fitzpatrick, J., Sonka, M.: Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE Med. Imag. 5370, 648–656 (2004)

    Google Scholar 

  68. Owen, C.G., Rudnicka, A.R., Nightingale, C.M., Mullen, R., Barman, S.A., Sattar, N., Cook, D.G., Whincup, P.H.: Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children. The Child Heart and Health Study in England (CHASE). Arterioscl. Throm. Vasc. Biol (2011)

    Google Scholar 

  69. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)

    Google Scholar 

  70. Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imag. 26(10), 1357–1365 (2007)

    Google Scholar 

  71. Serrarbassa, P.D., Dias, A.F., Vieira, M.F.: New concepts on diabetic retinopathy: neural versus vascular damage. Arq. Bras. Oftalmol. 71(3), 459–463 (2008)

    Article  Google Scholar 

  72. Shah, H.H.: Tehran problem solving by intelligent water drops. In: IEEE Congress on Evolutionary Computation (2007)

    Google Scholar 

  73. Soares, J.V.B., Leandro, J.J.G., Cesar, R.M., Jelinek Jr, H.F., Cree, M.J.: Retinal vessel segmentation using the 2D Gabor wavelet and supervised classification. IEEE Trans. Med. Imag. 25(9), 1214–1222 (2006)

    Google Scholar 

  74. Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23(4), 501–509 (2004)

    Article  Google Scholar 

  75. Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Google Scholar 

  76. Stutzle, T., Hoos, H.H.: MAXMIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)

    Google Scholar 

  77. Upeka, P., Jagath, S., Tarlochan, S.: A new biologically inspired optimization algorithm. In: Fourth International Conference on Industrial and Information Systems (ICIIS), Sri Lanka, pp. 28–31 (2009)

    Google Scholar 

  78. Verma, K., Deep, P., Ramakrishnan, A.G.: Detection and classification of diabetic retinopathy using retinal images. In: Annual IEEE India Conference (INDICON), pp. 1–6 (2011). doi:10.1109/INDCON.2011.6139346

  79. Vijayakumari, V., Suriyanarayanan, N.: Survey on the detection methods of blood vessel in retinal images. Eur. J. Sci. Res. 68(1), 83–92 (2012)

    Google Scholar 

  80. Wang, J.J., Taylor, B., Wong, T.Y., Chua, B., Rochtchina, E., Klein, R., Mitchell, P.: Retinal vessel diameters and obesity: a population-based study in older persons. Obesity (Silver Spring) 14(2), 206–14 (2006)

    Article  Google Scholar 

  81. Whitney, A.W.: A direct method of non-parametric measurement selection. IEEE Trans. Comput. 20, 1100–1103 (1971)

    Google Scholar 

  82. Xiaoyi, J., Mojon, D.: Adaptive local thresholding by verification based multi threshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 131–137 (2003)

    Google Scholar 

  83. Yang, X.S.: Fire fly algorithm for multimodal optimization. In: Proceedings of the stochastic Algorithms Foundations and Applications (SAGA 109), Lecture Notes in Computer Sciences, vol. 5792, Springer (2009)

    Google Scholar 

  84. You, X., Peng, Q., Yuan, Y., Cheung, Y., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn. 44(10–11), 2314–2324 (2011)

    Google Scholar 

  85. Zana, F., Klein, J.C.: A multimodal registration algorithm of eye fundus images using vessels detection and hough transform. IEEE Trans. Med. Imag. 18, 419–428 (1999)

    Google Scholar 

  86. Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10(7), 1010–1019 (2001)

    Google Scholar 

  87. Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 40, 438–445 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Sciences and Information Deptartment, Institute of Statistical Studies and Researches (ISSR), Scientific Research Group in Egypt (SRGE), Cairo University, Cairo, Egypt

    Ahmed Hamza Asad

  2. Information Technology Deptartment, Faculty of Computers and Information, Scientific Research Group in Egypt (SRGE), Cairo University, Cairo, Egypt

    Ahmed Hamza Asad & Aboul- Ella Hassaanien

Authors
  1. Ahmed Hamza Asad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aboul- Ella Hassaanien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aboul- Ella Hassaanien .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Information Systems and Computing, Brunel University, London, United Kingdom

    Crina Grosan

  3. Faculty of Computers and information, Ain Shams University, Cairo, Egypt

    Mohamed Fahmy Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Asad, A.H., Hassaanien, A.E. (2016). Retinal Blood Vessels Segmentation Based on Bio-Inspired Algorithm. In: Hassanien, AE., Grosan, C., Fahmy Tolba, M. (eds) Applications of Intelligent Optimization in Biology and Medicine. Intelligent Systems Reference Library, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-21212-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21212-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21211-1

  • Online ISBN: 978-3-319-21212-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation