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A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding
Research Article  ·  Published: 20 November 2025
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ICCK Transactions on Swarm and Evolutionary Learning
Volume 1, Issue 2, 2025: 50-82
Research Article Free to Read

A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding

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Omar Alvarez Omar Alvarez 1
Luis A. Beltran Luis A. Beltran 1 ORCID
Angel Casas-Ordaz Angel Casas-Ordaz 1 * ORCID
Jorge Ramos-Frutos Jorge Ramos-Frutos 2 ORCID
Mario A. Navarro-Velázquez Mario A. Navarro-Velázquez 1 ORCID
Oscar Ramos-Soto Oscar Ramos-Soto 1 ORCID
Diego Oliva Diego Oliva 1 ORCID
1 Depto. de Ingeniería Electro-Fotónica, Universidad de Guadalajara, CUCEI, Guadalajara 44430, Jalisco, México
2 Depto. de Ingeniería Industrial, Tecnológico Nacional de México, Jiquilpan 59514, Michoacán, México
* Corresponding Author: Angel Casas-Ordaz, [email protected]
Volume 1, Issue 2
Volume 1, Issue 2 2025
Received: 03 August 2025 Accepted: 17 September 2025 Published: 20 November 2025 CrossMark
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DOI: 10.62762/TSEL.2025.417356
ARK: ark:/57805/tsel.2025.417356
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Published in ICCK Transactions on Swarm and Evolutionary Learning
Volume/Issue Volume 1, Issue 2, 2025
Pages 50-82
Cited by 1 (Crossref)

Abstract

Metaheuristic Algorithms (MAs) are commonly used in the scope of digital image processing, in particular, image segmentation processes. This is evident in Multilevel Thresholding (MTH) methods, where the optimal threshold configuration must be found to produce high-quality segmented images. Minimum Cross-Entropy (MCE) is one of the most prominent techniques for MTH due to its simplicity and efficiency. This article proposes a comparison of recent MAs that have not yet been implemented for image segmentation. Six recently published MAs were implemented and tested on nine complicated images selected from the BSDS300 dataset. Analyzing the results reveals the best algorithm when MCE is used as the objective function. Central tendency indicators, such as Standard Deviation and mean, are also used to analyze the five threshold values. Additionally, three quality indicators used in processing images are analyzed: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Feature Similarity (FSIM). The result of this analysis allows for the quality of the segmentation of each algorithm used in the comparison. The metrics with the highest values are indicative of the most effective algorithm in terms of segmentation performance.

Graphical Abstract

A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding

Keywords

image segmentation thresholding minimum Cross-Entropy metaheuristics

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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  1. Zhiwei Feng, Benyi Yang, Baosong Deng. SAM-assisted multimodal collaborative enhancement for remote sensing image segmentation. Information Fusion, 2026 , 135 .
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* Citation data provided by Crossref Cited-by.

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APA Style
Alvarez, O., Beltran, L. A., Casas-Ordaz, A., Ramos-Frutos, J., Navarro-Velázquez, M. A., Ramos-Soto, O., & Oliva, D. (2025). A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding. ICCK Transactions on Swarm and Evolutionary Learning, 1(2), 50–82. https://doi.org/10.62762/TSEL.2025.417356
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TY  - JOUR
AU  - Alvarez, Omar
AU  - Beltran, Luis A.
AU  - Casas-Ordaz, Angel
AU  - Ramos-Frutos, Jorge
AU  - Navarro-Velázquez, Mario A.
AU  - Ramos-Soto, Oscar
AU  - Oliva, Diego
PY  - 2025
DA  - 2025/11/20
TI  - A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding
JO  - ICCK Transactions on Swarm and Evolutionary Learning
T2  - ICCK Transactions on Swarm and Evolutionary Learning
JF  - ICCK Transactions on Swarm and Evolutionary Learning
VL  - 1
IS  - 2
SP  - 50
EP  - 82
DO  - 10.62762/TSEL.2025.417356
UR  - https://www.icck.org/article/abs/TSEL.2025.417356
KW  - image segmentation
KW  - thresholding
KW  - minimum Cross-Entropy
KW  - metaheuristics
AB  - Metaheuristic Algorithms (MAs) are commonly used in the scope of digital image processing, in particular, image segmentation processes. This is evident in Multilevel Thresholding (MTH) methods, where the optimal threshold configuration must be found to produce high-quality segmented images. Minimum Cross-Entropy (MCE) is one of the most prominent techniques for MTH due to its simplicity and efficiency. This article proposes a comparison of recent MAs that have not yet been implemented for image segmentation. Six recently published MAs were implemented and tested on nine complicated images selected from the BSDS300 dataset. Analyzing the results reveals the best algorithm when MCE is used as the objective function. Central tendency indicators, such as Standard Deviation and mean, are also used to analyze the five threshold values. Additionally, three quality indicators used in processing images are analyzed: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Feature Similarity (FSIM). The result of this analysis allows for the quality of the segmentation of each algorithm used in the comparison. The metrics with the highest values are indicative of the most effective algorithm in terms of segmentation performance.
SN  - 3069-2962
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
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@article{Alvarez2025A,
  author = {Omar Alvarez and Luis A. Beltran and Angel Casas-Ordaz and Jorge Ramos-Frutos and Mario A. Navarro-Velázquez and Oscar Ramos-Soto and Diego Oliva},
  title = {A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding},
  journal = {ICCK Transactions on Swarm and Evolutionary Learning},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {50-82},
  doi = {10.62762/TSEL.2025.417356},
  url = {https://www.icck.org/article/abs/TSEL.2025.417356},
  abstract = {Metaheuristic Algorithms (MAs) are commonly used in the scope of digital image processing, in particular, image segmentation processes. This is evident in Multilevel Thresholding (MTH) methods, where the optimal threshold configuration must be found to produce high-quality segmented images. Minimum Cross-Entropy (MCE) is one of the most prominent techniques for MTH due to its simplicity and efficiency. This article proposes a comparison of recent MAs that have not yet been implemented for image segmentation. Six recently published MAs were implemented and tested on nine complicated images selected from the BSDS300 dataset. Analyzing the results reveals the best algorithm when MCE is used as the objective function. Central tendency indicators, such as Standard Deviation and mean, are also used to analyze the five threshold values. Additionally, three quality indicators used in processing images are analyzed: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Feature Similarity (FSIM). The result of this analysis allows for the quality of the segmentation of each algorithm used in the comparison. The metrics with the highest values are indicative of the most effective algorithm in terms of segmentation performance.},
  keywords = {image segmentation, thresholding, minimum Cross-Entropy, metaheuristics},
  issn = {3069-2962},
  publisher = {Institute of Central Computation and Knowledge}
}

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