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Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

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  • Abdul Kayom Md Khairuzzaman

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India)

  • Saurabh Chaudhury

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India)

Abstract

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

Suggested Citation

  • Abdul Kayom Md Khairuzzaman & Saurabh Chaudhury, 2017. "Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 8(4), pages 58-83, October.
    • Handle: RePEc:igg:jamc00:v:8:y:2017:i:4:p:58-83
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