Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms

Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages59-69
Number of pages11
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume825
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms'. Together they form a unique fingerprint.

  • Cite this

    Oliva, D., Abd Elaziz, M., & Hinojosa, S. (2019). Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms. In Studies in Computational Intelligence (pp. 59-69). (Studies in Computational Intelligence; Vol. 825). Springer Verlag. https://doi.org/10.1007/978-3-030-12931-6_6