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An Improved Fuzzy Clustering Segmentation Algorithm Based on Animal Behavior Global Optimization

  • A. AbsaraEmail author
  • S. N. Kumar
  • A. Lenin Fred
  • H. Ajay Kumar
  • V. Suresh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

The bio-inspired optimization algorithms play vital role in many research domains and this work analyzes animal behavior optimization algorithm. Medical image segmentation helps the physicians for disease diagnosis and treatment planning. This work incorporates ABO algorithm for cluster centroid selection in Fuzzy C-means clustering segmentation algorithm. The Animal Behavior Optimization (ABO) algorithm was developed based on the group behavior and was validated on 13 benchmark functions. The dominant nature of an animal species decides the fitness function value and each solution in problem space depicts the animal position. The ABO algorithm was coupled with the classical FCM for the analysis of region of interest in abdomen CT and brain MR datasets. The results were found to be efficient when compared with the FCM coupled with artificial bee colony (ABC), firefly, and cuckoo optimization algorithms. The promising results generated by ABC makes it an efficient one for real-world problems.

Keywords

Segmentation Animal behavior optimization Fuzzy C-means Artificial bee colony Firefly optimization Cuckoo optimization 

Notes

Acknowledgements

The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015).

References

  1. 1.
    Pardalos, P.M., Romeijn, H.E., Tuy, H.: Recent developments and trends in global optimization. J. Comput. Appl. Math. 124(1–2), 209–228 (2000).  https://doi.org/10.1016/S0377-0427(00)00425-8MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Floudas, C.A., Akrotirianakis, I.G., Caratzoulas, S.: Global optimization in the 21st century: advances and challenges. Comput. Chem. Eng. 29(6), 1185–1202 (2005).  https://doi.org/10.1016/j.compchemeng.2005.02.006CrossRefGoogle Scholar
  3. 3.
    Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014).  https://doi.org/10.1016/j.swevo.2013.11.003CrossRefGoogle Scholar
  4. 4.
    Singh, N., Singh, S.B.: A modified mean gray wolf optimization approach for benchmark and biomedical problems. Evol. Bioinform. 13 (2017).  https://doi.org/10.1177/2F1176934317729413
  5. 5.
    Hussein, W.A., Sahran, S., Sheikh Abdullah, S.N.H.: An improved Bees algorithm for real parameter optimization. Int. J. Adv. Comput. Sci. Appl. 6, 23–39 (2015)Google Scholar
  6. 6.
    Wang, B., Jin, X., Cheng, B.: Lion pride optimizer: an optimization algorithm inspired by lion pride behavior. Sci. China Inf. Sci. 55(10), 2369–2389 (2012).  https://doi.org/10.1007/s11432-012-4548-0MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Ruiz-Vanoye, J.A., Díaz-Parra, O., Cocón, F., Soto, A., Buenabad Arias, M.D.L.Á., Verduzco-Reyes, G., Alberto-Lira, R.: Meta-heuristics algorithms based on the grouping of animals by social behaviour for the traveling salesman problem. Int. J. Comb. Optim. Probl. Inf. 3(3), 104–123 (2012)Google Scholar
  8. 8.
    Cui, Z., Xu, Y., Zeng, J.: Social emotional optimization algorithm with random emotional selection strategy. In: Theory and New Applications of Swarm Intelligence. InTech. vol. 3, pp. 33–50 (2012)Google Scholar
  9. 9.
    Qin, Z.T.: Optimization Algorithms for Structured Machine Learning and Image Processing Problems. Columbia University (Thesis) (2013).  https://doi.org/10.7916/D8JH3TDM
  10. 10.
    Gao, H., Xu, W.: Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE Trans. Instrum. Meas. 59(4), 934–946 (2010).  https://doi.org/10.1109/TIM.2009.2030931CrossRefGoogle Scholar
  11. 11.
    Sanyal, N., Chatterjee, A., Munshi, S.: An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation. Expert Syst. Appl. 38(12), 15489 (2011).  https://doi.org/10.1016/j.eswa.2011.06.011CrossRefGoogle Scholar
  12. 12.
    Chu, X., Zhu, Y., Shi, J., Song, J.: Method of image segmentation based on fuzzy C-means clustering algorithm and artificial fish swarm algorithm. In: 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS), pp. 254–257. IEEE (2010).  https://doi.org/10.1109/ICISS.2010.5657199

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electronics and Communication EngineeringMar Ephraem College of Engineering and TechnologyMarthandamIndia
  2. 2.School of Computer Science and EngineeringMar Ephraem College of Engineering and TechnologyMarthandamIndia

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