An Otsu multi-thresholds segmentation algorithm based on improved ACO
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For the traditional multi-thresholds segmentation algorithms, usually it would take too much time in finding the optimal solution. As one of the widely used swarm-intelligence optimization algorithms, ant colony optimization (ACO) algorithm has been introduced to optimize the thresholding search process. The traditional ACO is improved in this paper to get a faster convergence speed and applied in Otsu multi-thresholds segmentation algorithms. When the ant colony is initialized, each member of the ant colony is distributed evenly in the solution space, so that it could search the entire solution space as fast as possible. In the search process, the random step length of ants moving is generated by the Lévy flight pattern, but the global transition probability of the traditional ACO is used to control the search range of the ant colony. The experimental results show that the proposed algorithm could obtain the optimal thresholds faster and more effectively than the traditional Otsu algorithm and the Otsu based on traditional ACO.
KeywordsOtsu segmentation Multi-thresholds segmentation Medical image segmentation ACO Lévy flight
This work is supported by the National Natural Science Foundation of China (61672259, 61602203), and Outstanding Young Talent Foundation of Jilin Province (20170520064JH).
- 1.Wang J, Xiaolei D, Zhou P (2017) Current situation and review of image segmentation. Recent Pat Comput Sci 10(1):70–79Google Scholar
- 8.Du KL, Swamy MNS (2016) Ant colony optimization. Search and optimization by metaheuristics. Cham, Birkhäuser, pp 191–199Google Scholar
- 11.Sharma E, Mahapatra P et al (2017) Image thresholding based on swarm intelligence technique for image segmentation. In: IEEE International Conference on Information Technology, pp 251–255Google Scholar
- 13.Lu J, Hu R (2012) A new image segmentation method based on Otsu method and ant colony algorithm. Int Conf Comput Sci Inf Process (CSIP) 2012:767–769Google Scholar
- 14.Han H, Zhifeng H, Chunguo W et al (2007) Analysis of convergence rate of ant colony algorithm. Chin J Comput 30(8):1344–1353Google Scholar
- 16.Gonzalez RC, Woods RE, Eddins SL (2013) Digital image processing-tenth chapter-image segmentation. Publishing House of Electronics Industry, BeijingGoogle Scholar
- 17.Dey S, Bhattacharyya S, Maulik U (2014) Quantum behaved multi-objective PSO and ACO optimization for multi-level thresholding. In: 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp 242–246Google Scholar
- 18.Mellal MA, Williams EJ (2017) A survey on ant colony optimization, particle swarm optimization, and cuckoo algorithms. In: Handbook of research on emergent applications of optimization algorithms, p~37Google Scholar
- 20.Wang Q, Guo X (2016) Levy flight-based particle swarm algorithm. Appl Res Comput 33(9):2588–2591Google Scholar
- 21.Pare S, Bhandari AK, Kumar A et al (2018) A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm. Comput Electr Eng 70(8):476–495Google Scholar