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Image multi-level-thresholding with Mayfly optimization

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 6 (2021)

DOI
10.11591/ijece.v11i6.pp5420-5429
ISSN
2722-2578, 2088-8708
Published
2021-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization (BFO), firefly-algorithm (FA), bat algorithm (BA), cuckoo search (CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work

Collaboration network of the authors

27 authors · 75 co-authorship links · 39 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Vignan's Foundation for Science, Technology & Research
  • Soonchunhyang University
  • Hindustan Institute of Technology and Science
  • Higher Colleges of Technology
  • Al-Mustaqbal University
  • Beirut Arab University
  • St. Joseph’s College of Engineering
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yunyoung NamSoonchunhyang University5
Lakshmana Kumar RamasamyHindustan Institute of Technology and Science4
Firoz KhanHigher Colleges of Technology4
Maytham N. MeqdadAl-Mustaqbal University4
R. Lakshmana KumarHindustan Institute of Technology and Science2
A. SaravananAffiliation unknown1
S. Sathya BamaAffiliation unknown1
Mohammed Zuhair Al-TaieUniversity of Technology Malaysia1
Joel Pinho LucasAffiliation unknown1
Jinesh AhamedHigher Colleges of Technology1
P. ChandanaVignan's Foundation for Science, Technology & Research1
G S Pradeep GhantasalaAffiliation unknown1
J. R. V. JenyVignan's Foundation for Science, Technology & Research1
Kaushik SekaranVignan's Foundation for Science, Technology & Research1
N. DeepikaVignan's Foundation for Science, Technology & Research1
Saif O. HusainIraqi University1
Alyaa Mohammed JawadAl-Mustaqbal University1
Ahlam R. KhekanUniversity of Information Technology and Communications1
Ganesh ChandrasekaranSri Eshwar College of Engineering1
M. Kalpana ChowdaryAffiliation unknown1
Jyothi Chinna BabuAffiliation unknown1
Ajmeera KiranAffiliation unknown1
Kotthuru Anil KumarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Image multi-level-thresholding with Mayfly optimization</dc:title><dc:creator>Kadry, Seifedine</dc:creator><dc:creator>Rajinikanth, Venkatesan</dc:creator><dc:creator>Koo, Jamin</dc:creator><dc:creator>Kang, Byeong-Gwon</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span&amp;gt;Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;(BFO), &amp;lt;/span&amp;gt;&amp;lt;span lang="EN-IN"&amp;gt;firefly-algorithm&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;(FA), bat algorithm (BA), cuckoo search&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i6.pp5420-5429</dc:identifier><dc:identifier>10.11591/ijece.v11i6.pp5420-5429</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 6 (2021); 5420</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/24227/15258</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>