Multi-objective Whale Optimization Algorithm for Multilevel Thresholding Segmentation

  • Mohamed Abd El Aziz
  • Ahmed A. Ewees
  • Aboul Ella Hassanien
  • Mohammed Mudhsh
  • Shengwu Xiong
Part of the Studies in Computational Intelligence book series (SCI, volume 730)


This chapter proposes a new method for determining the multilevel thresholding values for image segmentation. The proposed method considers the multilevel threshold as multi-objective function problem and used the whale optimization algorithm (WOA) to solve this problem. The fitness functions which used are the maximum between class variance criterion (Otsu) and the Kapur’s Entropy. The proposed method uses the whale algorithm to optimize threshold, and then uses this thresholding value to split the image. The experimental results showed the better performance of the proposed method to solving the multilevel thresholding problem for image segmentation and provided faster convergence with a relatively lower processing time.


Multi-objective Swarms optimization Whale optimization algorithm Multilevel thresholding Image segmentation 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mohamed Abd El Aziz
    • 1
  • Ahmed A. Ewees
    • 2
  • Aboul Ella Hassanien
    • 3
  • Mohammed Mudhsh
    • 4
  • Shengwu Xiong
    • 4
  1. 1.Faculty of Science, Department of MathematicsZagazig UniversityZagazigEgypt
  2. 2.Department of ComputerDamietta UniversityDamiettaEgypt
  3. 3.Faculty of Computers and Information, Information Technology DepartmentCairo UniversityGizaEgypt
  4. 4.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina

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