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Revolutionizing nonrigid demons registration with the whale optimization algorithm

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 2 (2025)

DOI
10.11591/ijece.v15i2.pp2372-2380
ISSN
2722-2578, 2088-8708
Published
2025-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA-which draws inspiration from whale hunting behavior-is integrated into the NDR architecture. The WOA-NDR approach intelligently explores the solution space, enhancing convergence and avoiding premature convergence. With the innovative WOA and NDR integration, the nonrigid image registration process is revolutionized and yields superior outcomes in terms of robustness and accuracy. The efficiency of the suggested strategy is demonstrated by experimental findings on a dataset of monomodal images. The obtained results are also compared with particle swarm optimization (PSO) based framework.

Collaboration network of the authors

8 authors · 26 co-authorship links · 8 across different organisations

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  • Seacom Skills University
  • Amity University
  • Haldia Institute of Technology
  • Osmania University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Sraddha Roy ChoudhuryOsmania University5
Bitan MisraAffiliation unknown5

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>Revolutionizing nonrigid demons registration with the whale optimization algorithm</dc:title><dc:creator>Roy, Abhisek</dc:creator><dc:creator>Roy, Pranab Kanti</dc:creator><dc:creator>Mitra, Anirban</dc:creator><dc:creator>Daw, Swarnali</dc:creator><dc:creator>Basu, Dipannita</dc:creator><dc:creator>Chakraborty, Sayan</dc:creator><dc:description>&lt;jats:p&gt;Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA-which draws inspiration from whale hunting behavior-is integrated into the NDR architecture. The WOA-NDR approach intelligently explores the solution space, enhancing convergence and avoiding premature convergence. With the innovative WOA and NDR integration, the nonrigid image registration process is revolutionized and yields superior outcomes in terms of robustness and accuracy. The efficiency of the suggested strategy is demonstrated by experimental findings on a dataset of monomodal images. The obtained results are also compared with particle swarm optimization (PSO) based framework.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-04-01</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.v15i2.pp2372-2380</dc:identifier><dc:identifier>10.11591/ijece.v15i2.pp2372-2380</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 2 (2025); 2372</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/36970/18155</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>