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Multi-level Image Thresholding Based on Hybrid Differential Evolution Algorithm. Application on Medical Images

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Metaheuristics for Medicine and Biology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 704))

Abstract

Image thresholding is definitely one of the most popular segmentation approaches for extracting objects from the background, or for discriminating objects from objects that have distinct gray-levels. It is typically simple and computationally efficient. It is based on the assumption that the objects can be distinguished by their gray levels.

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Authors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    M. Ali & M. Pant

  2. Laboratory of LISSI, Université Paris Est, Champs-sur-Marne, France

    P. Siarry

Authors
  1. M. Ali
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  2. P. Siarry
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  3. M. Pant
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Corresponding author

Correspondence to P. Siarry .

Editor information

Editors and Affiliations

  1. Signaux et Systèmes Intelligents, Université Paris Est Créteil, Laboratoire Images, Créteil, France

    Amir Nakib

  2. INRIA Lille Nord Europe Parc Scientifique de la Haute Borne, Laboratoire d'Informatique Fondamentale de Lille (UMR CNRS 8022), Villeneuve d'Ascq, France

    El-Ghazali Talbi

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Ali, M., Siarry, P., Pant, M. (2017). Multi-level Image Thresholding Based on Hybrid Differential Evolution Algorithm. Application on Medical Images. In: Nakib, A., Talbi, EG. (eds) Metaheuristics for Medicine and Biology. Studies in Computational Intelligence, vol 704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54428-0_2

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  • DOI: https://doi.org/10.1007/978-3-662-54428-0_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-54426-6

  • Online ISBN: 978-3-662-54428-0

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