Application of a Hybrid Ant Colony Optimization for the Multilevel Thresholding in Image Processing

  • Yun-Chia Liang
  • Angela Hsiang-Ling Chen
  • Chiuh-Cheng Chyu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


Our study proposes a hybrid optimization scheme based on an ant colony optimization algorithm with the Otsu method to render the optimal thresholding technique more applicable and effective. The properties of discriminate analysis in Otsu’s method are to analyze the separability among the gray levels in the image. The ACO-Otsu algorithm, a non-parametric and unsupervised method, is the first-known application of ACO to automatic threshold selection for image segmentation. The experimental results show that the ACO-Otsu efficiently speed up the Otsu’s method to a great extent at multi-level thresholding, and that such method can provide better effectiveness at population size of 20 for all given image types at multi-level thresholding in this study.


Optimal Threshold State Transition Probability Image Thresholding Otsu Method Multilevel Thresholding 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yun-Chia Liang
    • 1
  • Angela Hsiang-Ling Chen
    • 2
  • Chiuh-Cheng Chyu
    • 1
  1. 1.Department of Industrial Engineering and ManagementYuan Ze UniversityChung-Li, Taoyuan CountyTaiwan, R.O.C.
  2. 2.Department of Financial ManagementNanya Institute of TechnologyChung-Li, Taoyuan CountyTaiwan, R.O.C.

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