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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)00425-8
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.2005.02.006
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.2013.11.003
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
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)
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/s11432-012-4548-0
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)
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)
Qin, Z.T.: Optimization Algorithms for Structured Machine Learning and Image Processing Problems. Columbia University (Thesis) (2013). https://doi.org/10.7916/D8JH3TDM
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.2009.2030931
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.2011.06.011
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
Acknowledgements
The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Absara, A., Kumar, S.N., Lenin Fred, A., Ajay Kumar, H., Suresh, V. (2020). An Improved Fuzzy Clustering Segmentation Algorithm Based on Animal Behavior Global Optimization. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_60
Download citation
DOI: https://doi.org/10.1007/978-981-15-0035-0_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0034-3
Online ISBN: 978-981-15-0035-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)