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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 974024, 23 pages
Research Article

A Global Multilevel Thresholding Using Differential Evolution Approach

Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

Received 3 October 2013; Revised 23 January 2014; Accepted 3 February 2014; Published 20 March 2014

Academic Editor: Yi-Hung Liu

Copyright © 2014 Kanjana Charansiriphaisan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.