Skip to main content

Advertisement

Log in

Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm

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

Abstract

Due to the need of correct diseases analysis, MR image segmentation remains till now a challenging problem, especially in the presence of random noise. This paper proposes a new meta-heuristic algorithm for MR brain image segmentation, named Modified Shuffled Frog Leaping Algorithm (MSFLA), based on the technique of Shuffled Frog Leaping Algorithm (SFLA). In this new paradigm, there is no need to filter the original image. The new fitness function proposed in our algorithm helps to evaluate quickly the particle frogs in order to arrange them in descending order. The proposed approach has been compared with other meta-heuristics such as 3D-Otsu thresholding with SFLA and Genetic Algorithm (GA) and also with the algorithm of segmentation using the Rician Classifier (RiCE). Experimental results show that the proposed MSFLA is able to achieve better segmentation quality and execution time than the latest methods.

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

Access this article

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

Similar content being viewed by others

References

  1. Roy, S., Carass, A., Bazin, P.L., Resnick, S., Prince, J.L.: Consistent segmentation using a Rician classifier. Med. Image Anal. 16, 524–535 (2012)

    Article  Google Scholar 

  2. Hareef Naeem, H., Wang, L., DeLiang, L.: Segmentation of medical images using Legion. IEEE Trans. Med. Imaging 18(1), 74–91 (1999)

    Article  Google Scholar 

  3. Prastawa, M., Gilmore, J., Lin, W., Gerig, G.: Automatic segmentation of neonatal brain MRI, pp. 10–17. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI (2004)

  4. Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: Fully bayesian joint model for MR brain scan tissue and structure segmentation. Med. Image Comput. Comput.-Assist. Intervent. MICCAI 5242, 1066–1074 (2008)

    Google Scholar 

  5. Siyal, M.Y., Yu, L.: An intelligent modified fuzzy \(c\)-means based algorithm for bias estimation and segmentation of brain MRI. Patt. Recog. Lett. 26(13), 2052–2062 (2005)

    Article  Google Scholar 

  6. Nowak, R.D.: Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Process. 8(10), 1408–1419 (1999)

    Article  Google Scholar 

  7. Thacker, N.A., Pokri, M.: Noise filtering and testing for MR using a multi-dimensional partial volume model. In: 4\(^{th}\) Proceedings of the Medical Image Understanding and Analysis (MIUA ’04), pp. 21–24 (2004)

  8. Otsu, N.: A threshold selection method for grey level histograms. IEEE Trans. Syst. Man Cybern. SMC-9, pp. 62–66 (1979)

  9. Hammouche, K., Diaf, M., Siarry, P.: A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Elsevier Eng. Appl. Artif. Intell. 23(5), 676–688 (2010)

    Google Scholar 

  10. Karaboga, D., Basturk, B.: On the performance of artificial bee colony algorithm. Appl. Soft Comput. 687–697 (2008)

  11. Ma, H., Zhang, Y., Jia, G.: Medical images segmentation using modified genetic fuzzy clustering algorithm. Comput. Eng. Design[J] 23(13), 2357–2359 (2006)

    Google Scholar 

  12. Forghani, N., Forouzanfar, M., Forouzanfar, E.: MRI fuzzy segmentation of brain tissue using IFCM algorithm with particle swarm optimization. In: Computer and Information Sciences, Iscis 2007. 22nd International Symposium, Nov 7–9, pp. 1–4 (2007)

  13. Yeh, J.Y., Fu, J.C.: A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI. Expert Syst. Appl. 34(2), 1285–1295 (2008)

    Article  Google Scholar 

  14. Law, T.Y., Heng, P.A.: Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing. Med. Imaging 2000 Proc. SPIE 3979, 906–916 (2000)

    Article  Google Scholar 

  15. Hamdaoui, F., Ladgham, A., Sakly, A., Mtibaa, A.: A new images segmentation method based on modified PSO algorithm. Int. J. Imaging Syst. Technol. 23(3), 265–271 (2013)

    Article  Google Scholar 

  16. Du, F., Shi, W.K., Chen, L.Z., Deng, Y., Zhu, Z.F.: Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization. Pattern Recogn. Lett. 26(5), 597–603 (2005)

    Article  Google Scholar 

  17. Nakib, A., Roman, S., Oulhadj, H., Siarry, P.: Fast brain MRI segmentation based on two-dimensional survival exponential entropy and particle swarm optimization. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale. Lyon, France, Aug 23–26, pp. 5563–5566 (2007)

  18. Yang, C.S., Chuang, L.Y., Ke, C.H.: A combination of shuffled Frog–Leaping algorithm and genetic algorithm for gene selection. J. Adv. Comput. Intell. Intell. Inf. 12, 3 (2008)

    Google Scholar 

  19. Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plan. Manag. 129(3), 210–225 (2003)

    Article  Google Scholar 

  20. Alireza, R.V., Mostafa, D., Hamed, R., Ehsan, S.: A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem. Int. J. Adv. Manuf. Technol. 41, 1227–1239 (2008)

    Google Scholar 

  21. Huynh, T.H.: A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers. In: IEEE International Conference on Industrial Technology, Chengdu, 21–24 April 2008, pp. 1–6 (2008)

  22. Li, X., Liu, L., Wang, N., Pan, J.S.: A new robust watermarking scheme based on shuffled frog leaping algorithm. Intell. Autom. Soft Comput. 15, 1–15 (2011)

    Article  Google Scholar 

  23. Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Conf. Neural Netw. 4, 1942–1948 (1995)

    Google Scholar 

  24. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  25. Bhaduri, A.: Color image segmentation using clonal selection-based shuffled frog leaping algorithm. In: International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom (2009)

  26. Wang, N., Xia, L., Chen, X.H.: Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm. Pattern Recogn. Lett. 31, 1809–1815 (1979)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anis Ladgham.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ladgham, A., Hamdaoui, F., Sakly, A. et al. Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm. SIViP 9, 1113–1120 (2015). https://doi.org/10.1007/s11760-013-0546-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-013-0546-y

Keywords

Navigation