Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images

  • Abdalla Mostafa
  • Essam H. Houssein
  • Mohamed Houseni
  • Aboul Ella Hassanien
  • Hesham Hefny
Part of the Studies in Computational Intelligence book series (SCI, volume 730)


There is a remarkable increase in the popularity of swarms inspired algorithms in the last decade. It offers a kind of flexibility and efficiency in their applications in different fields. These algorithms are inspired by the behaviour of various swarms as birds, fish and animals. This chapter presents an overview of some algorithms as grey wolf optimization (GWO), artificial bee colony (ABC) and antlion optimization (ALO). It proposed swarm optimization approaches for liver segmentation based on these algorithms in CT and MRI images. The experimental results of these algorithms show that they are powerful and can get remarkable results when applied to segment liver medical images. It is evidently proved from the experimental results that ALO, GWO and ABC have obtained 94.49%, 94.08% and 93.73%, respectively, in terms of overall accuracy using similarity index measure.


Artificial bee colony Grey wolf Antlion and segmentation 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Abdalla Mostafa
    • 1
  • Essam H. Houssein
    • 2
  • Mohamed Houseni
    • 3
  • Aboul Ella Hassanien
    • 4
  • Hesham Hefny
    • 1
  1. 1.Scientific Research Group in Egypt (SRGE)Institute of Statistical Studies and Research, Cairo UniversityGizaEgypt
  2. 2.Scientific Research Group in Egypt (SRGE), Faculty of Computers and InformationMinia UniversityMiniaEgypt
  3. 3.National Liver Institute, Radiology DepartmentMenofia UniversityMenofiaEgypt
  4. 4.Faculty of Computers and Information, Information Technology DepartmentCairo UniversityGizaEgypt

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