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Image segmentation using multilevel thresholding based on modified bird mating optimization

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Abstract

Multilevel thresholding using Otsu or Kapur methods is widely used in the context of image segmentation. These methods select optimal thresholds in gray level images by maximizing between-class variance or entropy criterion. These methods become time consuming and less efficient with increasing number of thresholds. To increase the efficiency of the image segmentation using multilevel thresholding based on Kapur and Otsu methods, we developed a hybrid optimization algorithm named BMO-DE based on bird mating optimization (BMO) and differential evolutionary (DE) algorithms. The efficiency of the proposed method was evaluated on eight standard benchmark images. The proposed method achieved better segmentation results in term of solution quality and stability in comparison with other well-known techniques including bacterial foraging (BF), modified bacterial foraging (MBF), particle swarm optimization (PSO), genetic algorithm (GA) and hybrid algorithm named PSO-DE.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Maliheh Ahmadi, Kamran Kazemi, Taher Niknam & Mohammad Sadegh Helfroush

  2. Laboratory of Functional Neuroscience and Pathologies (LFNP, EA4559), University Research Center (CURS), CHU AMIENS - SITE SUD, Avenue Laënnec, 80420, Salouël, France

    Ardalan Aarabi

  3. Faculty of Medicine, University of Picardie Jules Verne, 80036, Amiens, France

    Ardalan Aarabi

Authors
  1. Maliheh Ahmadi
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  2. Kamran Kazemi
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  3. Ardalan Aarabi
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  4. Taher Niknam
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  5. Mohammad Sadegh Helfroush
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Correspondence to Kamran Kazemi.

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Ahmadi, M., Kazemi, K., Aarabi, A. et al. Image segmentation using multilevel thresholding based on modified bird mating optimization. Multimed Tools Appl 78, 23003–23027 (2019). https://doi.org/10.1007/s11042-019-7515-6

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  • DOI: https://doi.org/10.1007/s11042-019-7515-6

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