Skip to main content
SpringerLink
Log in

Image Thresholding Based on Fuzzy Particle Swarm Optimization

  • Chapter
  • First Online:
Hybrid Metaheuristics for Image Analysis

Abstract

Segmentation is a crucial stage in the image analysis process, whose main purpose is to partition an image into meaningful regions of interest. Thresholding is the simplest image segmentation method, where a global or local threshold value is selected for segmenting pixels into background and foreground regions. However, the determination of a proper threshold value is typically dependent on subjective assumptions or empirical rules. In this work, we propose and analyze an image thresholding technique based on a fuzzy particle swarm optimization. Several images are used in our experiments to show the effectiveness of the developed approach.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Notes

  1. 1.

    U i(Ï•) is an array of random numbers uniformly distributed in the range [0, Ï•].

References

  1. M.N. Ab Wahab, S. Nefti-Meziani, A. Atyabi, A comprehensive review of swarm optimization algorithms. PloS One 10(5), e0122827 (2015)

    Google Scholar 

  2. A. Abraham, H. Guo, H. Liu, Swarm intelligence: foundations, perspectives and applications, in Swarm Intelligent Systems (Springer, Berlin, 2006), pp. 3–25

    Google Scholar 

  3. M. Ali, C.W. Ahn, M. Pant, Multi-level image thresholding by synergetic differential evolution. Appl. Soft Comput. 17, 1–11 (2014)

    Google Scholar 

  4. A. Alihodzic, M. Tuba, Improved bat algorithm applied to multilevel image thresholding. Sci. World J. 2014, 16 pp. (2014)

    Google Scholar 

  5. D.T. Anderson, T.C. Havens, C. Wagner, J.M. Keller, M.F. Anderson, D.J. Wescott, Sugeno fuzzy integral generalizations for sub-normal fuzzy set-valued inputs, in IEEE International Conference on Fuzzy Systems (2012), pp. 1–8

    Google Scholar 

  6. M. Beauchemin, Image thresholding based on semivariance. Pattern Recogn. Lett. 34(5), 456–462 (2013)

    Google Scholar 

  7. J. Bernsen, Dynamic thresholding of grey-level images, in 6th International Conference on Pattern Recognition, Berlin (1986), pp. 1251–1255

    Google Scholar 

  8. B. Bhanu, S. Lee, Genetic Learning for Adaptive Image Segmentation, vol. 287 (Springer Science & Business Media, Berlin, 2012)

    Google Scholar 

  9. B. Bhanu, S. Lee, S. Das, Adaptive image segmentation using genetic and hybrid search methods. IEEE Trans. Aerosp. Electron. Syst. 31(4), 1268–1291 (1995)

    Google Scholar 

  10. I. Brajevic, M. Tuba, Cuckoo search and firefly algorithm applied to multilevel image thresholding, in Cuckoo Search and Firefly Algorithm (Springer, Berlin, 2014), pp. 115–139

    Google Scholar 

  11. D. Bratton, J. Kennedy, Defining a standard for particle swarm optimization, in IEEE Swarm Intelligence Symposium (IEEE, New York, 2007), pp. 120–127

    Google Scholar 

  12. K. Charansiriphaisan, S. Chiewchanwattana, K. Sunat, A global multilevel thresholding using differential evolution approach. Math. Probl. Eng. 2014, 23 pp. (2014)

    Google Scholar 

  13. M. Clerc, Particle Swarm Optimization (Wiley, New York, 2010)

    Google Scholar 

  14. J. D’Avy, W.-W. Hsu, C.-H. Chen, A. Koschan, M. Abidi, An efficient method for optimizing segmentation parameters, in Emerging Technologies in Intelligent Applications for Image and Video Processing (IGI Global, Hershey, 2016), pp. 29–47

    Google Scholar 

  15. A. Dirami, K. Hammouche, M. Diaf, P. Siarry, Fast multilevel thresholding for image segmentation through a multiphase level set method. Signal Process. 93(1), 139–153 (2013)

    Google Scholar 

  16. D.J. Dubois, Fuzzy Sets and Systems: Theory and Applications, vol. 144 (Academic, New York, 1980)

    Google Scholar 

  17. R.C. Eberhart, Y. Shi, J. Kennedy, Swarm Intelligence (Elsevier, Amsterdam, 2001)

    Google Scholar 

  18. X. Gao, T. Wang, J. Li, A content-based image quality metric, in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (Springer, Berlin, 2005), pp. 231–240

    Google Scholar 

  19. C.A. Glasbey, An analysis of histogram-based thresholding algorithms. Graph. Models Image Process. 55(6), 532–537 (1993)

    Google Scholar 

  20. R. Gonzalez, R. Woods, Digital Image Processing Using Matlab (McGraw Hill Education, New York, 2010)

    Google Scholar 

  21. M. Grabisch, M. Sugeno, T. Murofushi, Fuzzy Measures and Integrals: Theory and Applications (Springer, New York, 2000)

    Google Scholar 

  22. J.N. Kapur, P.K. Sahoo, A.K. Wong, A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)

    Google Scholar 

  23. J. Kennedy, R. Eberhart, Particle swarm optimization, in IEEE International Conference on Neural Networks, Perth, 1995, pp. 1942–1948

    Google Scholar 

  24. G. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic, vol. 4 (Prentice Hall, Princeton, 1995)

    Google Scholar 

  25. T. Kurban, P. Civicioglu, R. Kurban, E. Besdok, Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl. Soft Comput. 23, 128–143 (2014)

    Google Scholar 

  26. Y. Li, L. Jiao, R. Shang, R. Stolkin, Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Inform. Sci. 294, 408–422 (2015)

    Google Scholar 

  27. Y.-C. Liang, A.H.-L. Chen, C.-C. Chyu, Application of a hybrid ant colony optimization for the multilevel thresholding in image processing, in International Conference on Neural Information Processing (Springer, Berlin, 2006), pp. 1183–1192

    Google Scholar 

  28. Y. Liu, C. Mu, W. Kou, Optimal multilevel thresholding using the modified adaptive particle swarm optimization. Int. J. Digital Content Technol. Appl. 6(15), 208–219 (2012)

    Google Scholar 

  29. Y. Liu, C. Mu, W. Kou, J. Liu, Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Comput. 19(5), 1311–1327 (2015)

    Google Scholar 

  30. A.R. Malisia, H.R. Tizhoosh, Image thresholding using ant colony optimization, in 3rd Canadian Conference on Computer and Robot Vision (2006)

    Google Scholar 

  31. S. Manikandan, K. Ramar, M.W. Iruthayarajan, K. Srinivasagan, Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm. Measurement 47, 558–568 (2014)

    Google Scholar 

  32. T. Murofushi, M. Sugeno, Fuzzy measures and fuzzy integrals, in Fuzzy Measures and Integrals – Theory and Applications, ed. by M. Grabisch, T. Murofushi, M. Sugeno (Physica, Heidelberg, 2000), pp. 3–41

    Google Scholar 

  33. W. Niblack, An Introduction to Digital Image Processing (Prentice Hall, Princeton, 1986)

    Google Scholar 

  34. D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, M. Perez-Cisneros, Multilevel thresholding segmentation based on harmony search optimization. J. Appl. Math. 2013 (2013)

    Google Scholar 

  35. N. Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  36. R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  37. A. Rosenfeld, Multiresolution Image Processing and Analysis, vol. 12 (Springer Science & Business Media, Berlin, 2013)

    MATH  Google Scholar 

  38. S. Sarkar, S. Das, Multilevel image thresholding based on 2D histogram and maximum tsallis entropy: a differential evolution approach. IEEE Trans. Image Process. 22(12), 4788–4797 (2013)

    Article  MathSciNet  Google Scholar 

  39. S. Sarkar, S. Das, S.S. Chaudhuri, A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn. Lett. 54, 27–35 (2015)

    Article  Google Scholar 

  40. J. Sauvola, M. Pietaksinen, Adaptive document image binarization. Pattern Recogn. 33, 225–236 (2000)

    Article  Google Scholar 

  41. M. Sezgin, Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)

    Article  MathSciNet  Google Scholar 

  42. S. Shen, W. Sandham, M. Granat, A. Sterr, MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans. Inform. Technol. Biomed. 9(3), 459–467 (2005)

    Article  Google Scholar 

  43. Y. Shi, R. Eberhart, A modified particle swarm optimizer, in IEEE International Conference on Evolutionary Computation (IEEE, New York, 1998), pp. 69–73

    Google Scholar 

  44. A. Singla, S. Patra, A context sensitive thresholding technique for automatic image segmentation, in Computational Intelligence in Data Mining, ed. by L.C. Jain, H.S. Behera, J.K. Mandal, D.P. Mohapatra. Smart Innovation, Systems and Technologies, vol. 32, (Springer India, New Delhi, 2015), pp. 19–25

    Google Scholar 

  45. M. Sugeno, Theory of fuzzy integrals and its applications. Ph.D. thesis, Tokyo Institute of Technology, 1974

    Google Scholar 

  46. I.C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection. Inform. Process. Lett. 85(6), 317–325 (2003)

    Article  MathSciNet  Google Scholar 

  47. B.D. Trier, A.K. Jain, Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Anal. Mach. Intell. 17(12), 1191–1201 (1995)

    Article  Google Scholar 

  48. Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  49. J.S. Weszka, R.N. Nagel, A. Rosenfeld, A threshold selection technique. IEEE Trans. Comput. C-23, 1322–1326 (1974)

    Article  Google Scholar 

  50. Q.-Z. Ye, P.-E. Danielsson, On minimum error thresholding and its implementations. Pattern Recogn. Lett. 7(4), 201–206 (1988)

    Article  Google Scholar 

  51. Z. Ye, Z. Hu, X. Lai, H. Chen, Image segmentation using thresholding and swarm intelligence. J. Softw. 7(5), 1074–1082 (2012)

    Article  Google Scholar 

  52. P.-Y. Yin, Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007)

    MathSciNet  MATH  Google Scholar 

  53. Y. Zou, H. Liu, E. Song, Z. Huang, Image bilevel thresholding based on multiscale gradient multiplication. Comput. Electr. Eng. 38(4), 853–861 (2012)

    Article  Google Scholar 

  54. Y. Zou, H. Liu, Q. Zhang, Image bilevel thresholding based on stable transition region set. Digital Signal Process. 23(1), 126–141 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are thankful to São Paulo Research Foundation (grant FAPESP #2014/12236-1) and the Brazilian Council for Scientific and Technological Development (grant CNPq #305169/2015-7 and scholarship #141647/2017-5) for their financial support.

Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, SP, Brazil

    Anderson Carlos Sousa Santos & Helio Pedrini

Authors
  1. Anderson Carlos Sousa Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Helio Pedrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anderson Carlos Sousa Santos .

Editor information

Editors and Affiliations

  1. Department of Computer Applications, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Santos, A.C.S., Pedrini, H. (2018). Image Thresholding Based on Fuzzy Particle Swarm Optimization. In: Bhattacharyya, S. (eds) Hybrid Metaheuristics for Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-77625-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77625-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77624-8

  • Online ISBN: 978-3-319-77625-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation