Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

Xiuteng Shu; Xiangmeng Tang

doi:doi:10.11648/j.acis.20241203.12

Research Article |

| Peer-Reviewed

Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

Xiuteng Shu

, Xiangmeng Tang^*

Published in Automation, Control and Intelligent Systems (Volume 12, Issue 3)

Received: 25 June 2024 Accepted: 20 August 2024 Published: 27 August 2024

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

Published in	Automation, Control and Intelligent Systems (Volume 12, Issue 3)
DOI	10.11648/j.acis.20241203.12
Page(s)	60-70
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Image Segmentation, Harmony Search, Otsu, Multi-threshold

References

[1]	Sakshi, Kukreja V. Image Segmentation Techniques: Statistical, Comprehensive, Semi-Automated Analysis and an Application Perspective Analysis of Mathematical Expressions [J]. Archives of Computational Methods in Engineering, 2023, 30(1): 457-495. https://doi.org/10.1007/s11831-022-09805-9
[2]	Jiang Z, Zou F, Chen D B, et al. An ensemble multi-swarm teaching-learning-based optimization algorithm for function optimization and image segmentation [J]. Appl. Soft Comput. 2022, 130: 109653. https://doi.org/10.1016/j.asoc.2022.109653
[3]	Wu, Qiang. Microscope Image Processing \|\| Image Segmentation [J]. 2008: 159-194. https://doi.org/10.1016/B978-0-12-372578-3.00009-X
[4]	Abualigah L, Almotairi K H, Elaziz M A. Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: comparative analysis, open challenges and new trends [J]. Applied Intelligence, 2022: 1-51. https://doi.org/10.1007/s10489-022-04064-4
[5]	Gao H, Shi Y, Pun C M, et al. An Improved Artificial Bee Colony Algorithm With its Application [J]. IEEE transactions on industrial informatics, 2019(4). https://doi.org/10.1109/tii.2018.2857 -198
[6]	Otsu N. A Threshold Selection Method from Gray-Level Histograms [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. https://doi.org/10.1109/TSMC.1979.431 0076
[7]	Qin J, Shen X, Mei F, et al. An Otsu multi-thresholds segmentation algorithm based on improved ACO [J]. The Journal of Supercomputing, 2019, 75(2): 955-967. https://doi.org/10.1007/s-11227-018-2622-0
[8]	Al-Rahlawee A T H, Rahebi J. Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm [J]. Multimedia Tools and Applications, 2021, 80(2–3). https://doi.org/10.1007/s11042-021-10860-w
[9]	Banerjee S, Mitra S, Uma Shankar B. Single seed delineation of brain tumor using multi-thresholding [J]. Information Sciences, 2016: 88-103. https://doi.org/10.1016/j.ins.2015.10.018
[10]	Huang C, Li X, Wen Y. AN OTSU image segmentation based on fruitfly optimization algorithm [J]. AEJ - Alexandria Engineering Journal, 2020. https://doi.org/10.1016/j.aej.2020.06.054
[11]	Pare S, Kumar A, Singh G K, et al. Image Segmentation Using Multilevel Thresholding: A Research Review [J]. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2020, 44(1): 1-29. https://doi.org/10.1007/s40998-019 -00251-1
[12]	Shu-Liang W, He-Ji Z. Multilevel thresholding gray-scale image segmentation based on improved particle swarm optimization [J]. Journal of Computer Applications, 2012. https://doi.org/10.1007/978-3-642-25538-0_26
[13]	Sathya P D, Kayalvizhi R. Modified bacterial foraging algorithm based multilevel thresholding for image segmentation [J]. Engineering Applications of Artificial Intelligence, 2011, 24(4): 595-615. https://doi.org/10.1016/j.engappai.2010.12.001
[14]	Geem Z W, Kim J H, Loganathan G V. A New Heuristic Optimization Algorithm: Harmony Search [J]. Simulation, 2001, 2(2): 60-68. https://doi.org/10.1177/003754970107600201
[15]	Zhu Q, Tang X, Li Y, et al. An improved differential-based harmony search algorithm with linear dynamic domain [J]. Knowledge-Based Systems, 2020, 187(Jan.): 104809.1 -104809.14. https://doi.org/10.1016/j.knosys.2019.06.017
[16]	Kang J, Kwon S, Ryu D, et al. HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software [J]. Applied Sciences, 2021, 11(5): 2002. https://doi.org/10.3390/app11052002
[17]	Dubey M, Kumar V, Kaur M, et al. A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications [J]. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/5594267
[18]	Cheng M Y, Prayogo D, Wu Y W, et al. A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure [J]. Automation in Construction, 2016, 69(SEP.): 21 -33. https://doi.org/10.1016/j.autcon.2016.05.023
[19]	Kumar V, Chhabra J K, Kumar D. Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems [J]. Journal of Computational Science, 2014, 5(2): 144-155. https://doi.org/10.1016/j.jocs.2013.12.001
[20]	Xiang W L, An M Q, Li Y Z, et al. An improved global-best harmony search algorithm for faster optimization [J]. Expert Systems with Applications, 2014, 41(13): 5788-5803. https://doi.org/10.1016/j.eswa.2014.03.016

Cite This Article

Plain Text BibTeX RIS

APA Style

Shu, X., Tang, X. (2024). Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm. Automation, Control and Intelligent Systems, 12(3), 60-70. https://doi.org/10.11648/j.acis.20241203.12

Copy | Download

ACS Style

Shu, X.; Tang, X. Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm. Autom. Control Intell. Syst. 2024, 12(3), 60-70. doi: 10.11648/j.acis.20241203.12

Copy | Download

AMA Style

Shu X, Tang X. Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm. Autom Control Intell Syst. 2024;12(3):60-70. doi: 10.11648/j.acis.20241203.12

Copy | Download

@article{10.11648/j.acis.20241203.12,
  author = {Xiuteng Shu and Xiangmeng Tang},
  title = {Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm
},
  journal = {Automation, Control and Intelligent Systems},
  volume = {12},
  number = {3},
  pages = {60-70},
  doi = {10.11648/j.acis.20241203.12},
  url = {https://doi.org/10.11648/j.acis.20241203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20241203.12},
  abstract = {Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

AU  - Xiuteng Shu
AU  - Xiangmeng Tang
Y1  - 2024/08/27
PY  - 2024
N1  - https://doi.org/10.11648/j.acis.20241203.12
DO  - 10.11648/j.acis.20241203.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 60
EP  - 70
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20241203.12
AB  - Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

VL  - 12
IS  - 3
ER  -

Copy | Download

Author Information

Xiuteng Shu

School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China

http://orcid.org/0009-0005-4467-9259
Xiangmeng Tang

School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China

Contact Email

http://orcid.org/0000-0002-4279-6567

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Shu, X., Tang, X. (2024). Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm. Automation, Control and Intelligent Systems, 12(3), 60-70. https://doi.org/10.11648/j.acis.20241203.12

Copy | Download

ACS Style

Shu, X.; Tang, X. Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm. Autom. Control Intell. Syst. 2024, 12(3), 60-70. doi: 10.11648/j.acis.20241203.12

Copy | Download

AMA Style

Shu X, Tang X. Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm. Autom Control Intell Syst. 2024;12(3):60-70. doi: 10.11648/j.acis.20241203.12

Copy | Download

@article{10.11648/j.acis.20241203.12,
  author = {Xiuteng Shu and Xiangmeng Tang},
  title = {Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm
},
  journal = {Automation, Control and Intelligent Systems},
  volume = {12},
  number = {3},
  pages = {60-70},
  doi = {10.11648/j.acis.20241203.12},
  url = {https://doi.org/10.11648/j.acis.20241203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20241203.12},
  abstract = {Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

AU  - Xiuteng Shu
AU  - Xiangmeng Tang
Y1  - 2024/08/27
PY  - 2024
N1  - https://doi.org/10.11648/j.acis.20241203.12
DO  - 10.11648/j.acis.20241203.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 60
EP  - 70
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20241203.12
AB  - Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

VL  - 12
IS  - 3
ER  -

Copy | Download