Skip to main content

Advertisement

SpringerLink
Log in

Fuzzy-based artificial bee colony optimization for gray image segmentation

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this article, we have proposed an image segmentation algorithm FABC, which is a kind of unsupervised classification (clustering), where we combine the concept of artificial bee colony optimization (ABC) and the popular fuzzy C means (FCM) and named it as fuzzy-based ABC or FABC. In FABC, we have used fuzzy membership function to search for optimum cluster centers using ABC. FABC is more efficient than other optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and expectation maximization (EM) algorithms. FABC overcomes the drawbacks of FCM as it does not depend on the choice of initial cluster centers and it performs better in terms of convergency, time complexity, robustness and segmentation accuracy. FABC becomes more efficient as it takes the advantage of the randomized characteristics of ABC for the initialization of the cluster centers. The experiments with FABC, GA, PSO and EM have been done over various grayscale images including some synthetic, medical and texture images, and segmentation of such images is very difficult due to the low contrast, noise and other imaging ambiguities. The efficiency of FABC is proven by both quantitative and qualitative measures.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338353 (1965)

    Article  MathSciNet  Google Scholar 

  2. Tolias, Y.A., Panas, S.M.: On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. IEEE Signal Proces. Lett. 5, 245247 (1998)

    Article  Google Scholar 

  3. Tolias, Y.A., Panas, S.M.: Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Trans. Syst. Man Cybern. 28, 359369 (1998)

  4. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21, 193199 (2002)

    Article  Google Scholar 

  5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    Book  MATH  Google Scholar 

  6. Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)

    Article  Google Scholar 

  7. Bose, A., Mali, K.: A Comparative Study on Image Segmentation Based on Artificial Bee Colony Optimization and FCM. IJARCSSE. ISSN: 2277 128X (2014)

  8. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR0 6, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

  9. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687697 (2008)

    Google Scholar 

  10. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108132 (2009)

    MathSciNet  MATH  Google Scholar 

  11. Karaboga, D., Akay, B.: A survey on the applications of artificial bee colony in signal, image, and video processing. Signal Image Video Process. (2015). doi:10.1007/s11760-015-0758-4

    Google Scholar 

  12. Lin, J.-S., Wu, S.-H.: Fuzzy artificial bee colony system with cooling schedule for the segmentation of medical images by using of spatial information. Res. J. Appl. Sci. Eng. Technol. 5(05), 1483–1490 (2013)

    Google Scholar 

  13. Salima, O., Taleb-Ahmed, A., Mohamed, B.: Spatial information based image clustering with a swarm approach. IAES Int. J. Artif. Intell. (IJ-AI) 1(3), 149 (2012)

    Google Scholar 

  14. Emary, E., Zawbaa, H.M., Hassanien, A.E., Schaefer, G., Azar, A.T.: Retinal blood vessel segmentation using bee colony optimisation and pattern search. In: Neural Networks (IJC NN), 2014 International Joint Conference, pp. 1001–1006 (2014)

  15. Base of 300 synthetic images. http://www.ensibourges.fr/LVR/SIV/

  16. Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)

    Google Scholar 

  17. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)

    MATH  Google Scholar 

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference Neural Network, vol. 4, pp. 1942–1948 (1995)

  19. Park, H., Ozeki, T.: Singularity and slow convergence of the EM algorithm for Gaussian mixtures. Neural Process. Lett. 29, 4559 (2009)

    Article  Google Scholar 

  20. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 224227 (1979)

    Google Scholar 

  21. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13, 841847 (1991)

    Article  Google Scholar 

  22. Pal, S.K., Ghosh, A., Shankar, B.U.: Segmentation with remotely sensed images with fuzzy thresholding, and quantitative evaluation. Int. J. Remote Sens. 21(11), 22692300 (2000)

    Article  Google Scholar 

  23. Dunn, J.: Well separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

We thank the anonymous referees for their valuable comments and suggestions, which helps us to improve the paper.

Author information

Authors and Affiliations

  1. University of Kalyani, Kalyani, India

    Ankita Bose & Kalyani Mali

Authors
  1. Ankita Bose
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kalyani Mali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankita Bose.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bose, A., Mali, K. Fuzzy-based artificial bee colony optimization for gray image segmentation. SIViP 10, 1089–1096 (2016). https://doi.org/10.1007/s11760-016-0863-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0863-z

Keywords

Search

Navigation