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Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms

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Metaheuristic Algorithms for Image Segmentation: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 825))

Abstract

Multilevel image thresholding is effectually technique used to segment many types of images. It usually applied in image preprocessing phase. In this chapter, a review of gray level image segmentation using multilevel thresholding based on metaheuristic algorithms is introduced. Nine algorithms and their studies in multilevel thresholding segmentation are presented namely cuckoo search, bat algorithm, artificial bee colony, particle swarm optimization, firefly algorithm, social spider optimization algorithm, whale optimization algorithm, moth-flame optimization algorithm, and gray wolf optimization algorithm. The objective function, performance measures, and the number of images and thresholds that applied on the studies are mentioned. The review concludes that the multilevel thresholding segmentation is a challenge and many studies till now work to solve it.

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Author information

Authors and Affiliations

  1. Departamento de Electrónica, CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  2. Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd Elaziz

  3. Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

    Salvador Hinojosa

Authors
  1. Diego Oliva
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  2. Mohamed Abd Elaziz
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  3. Salvador Hinojosa
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Corresponding author

Correspondence to Diego Oliva .

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Oliva, D., Abd Elaziz, M., Hinojosa, S. (2019). Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms. In: Metaheuristic Algorithms for Image Segmentation: Theory and Applications. Studies in Computational Intelligence, vol 825. Springer, Cham. https://doi.org/10.1007/978-3-030-12931-6_6

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