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A Survey of Image Segmentation based on Evolutionary Computation and Clustering Techniques

by J. Hajiram Beevi, O.A. Mohamed Jafar, A.R. Mohamed Shanavas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 80
Year of Publication: 2025
Authors: J. Hajiram Beevi, O.A. Mohamed Jafar, A.R. Mohamed Shanavas
10.5120/ijca2025924739

J. Hajiram Beevi, O.A. Mohamed Jafar, A.R. Mohamed Shanavas . A Survey of Image Segmentation based on Evolutionary Computation and Clustering Techniques. International Journal of Computer Applications. 186, 80 ( Apr 2025), 11-18. DOI=10.5120/ijca2025924739

@article{ 10.5120/ijca2025924739,
author = { J. Hajiram Beevi, O.A. Mohamed Jafar, A.R. Mohamed Shanavas },
title = { A Survey of Image Segmentation based on Evolutionary Computation and Clustering Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2025 },
volume = { 186 },
number = { 80 },
month = { Apr },
year = { 2025 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number80/a-survey-of-image-segmentation-based-on-evolutionary-computation-and-clustering-techniques/ },
doi = { 10.5120/ijca2025924739 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-04-26T02:19:35.277636+05:30
%A J. Hajiram Beevi
%A O.A. Mohamed Jafar
%A A.R. Mohamed Shanavas
%T A Survey of Image Segmentation based on Evolutionary Computation and Clustering Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 80
%P 11-18
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation is treated as the fundamental problem in image processing. Its execution has a notable impact on further analysis. However, there exist many algorithms and approaches for image segmentation. Evolutionary Computation Algorithms have been given considerable attention in the area of image segmentation due to their capability in providing optimal solutions for many practical applications. Meanwhile, Clustering is also one of the commonly used image segmentation techniques. There are rarely any exhaustive surveys on Evolutionary Computation and Clustering Algorithms based on image segmentation methods, which can entitle the researchers to obtain a quick perception of these areas and compare the existing methods. Therefore, this survey briefly discusses some of the works done by the researchers using Evolutionary Computation and Clustering Algorithms. This survey leads to the conclusion that the field of Evolutionary Computation is growing fast. The continuous advancement of Evolutionary Computation will surely help to resolve many complex image segmentation tasks in the future.

References
  1. Zaitoun, N. M., and Aqel, M. J. 2015. Survey on Image Segmentation Techniques. Procedia Computer Science, 65, 797–806.
  2. Dubey, S. K., Vijay, S., and Pratibha. 2018. A Review of Image Segmentation using Clustering Methods. International Journal of Applied Engineering Research, 13(5), 2484–2489.
  3. Liang, Y., Zhang, M., and Browne, W. N. 2014. Image Segmentation: a survey of methods based on evolutionary computation. In Lecture notes in computer science pp. 847–859.
  4. Saini, S., and Arora, K. 2014. A Study Analysis on the Different Image Segmentation Techniques. International Journal of Information & Computation Technology, 4(14), 1445–1452.
  5. Rajalakshmi, N. T. and Senthilnathan, N. R. 2023. Dataset and Performance Metrics towards Semantic Segmentation. International Journal of Engineering and Management Research, 13(1):40–49.
  6. Vlasceanu, G. V., Tarba, N., Voncila, M. L., Boiangiu, C. A., and Teodor, D. F. 2024. Selecting the right metric: A detailed study on image segmentation evaluation. Brain Broad Research in Artificial Intelligence and Neuroscience, 15(4):295.
  7. Sara, U., Akter, M., and Uddin, M.S. 2019. Image Quality Assessment through FSIM, SSIM, MSE and PSNR A Comparative Study. Journal of Computer and Communications, 07(03):8–18.
  8. Berkhin, P. 2006. A Survey of Clustering Data Mining Techniques. In: Kogan J, Nicholas C, TeboulleM (eds) Grouping Multidimensional Data, pp. 25–71.
  9. Aslam, Y., N, S., N, R., and K, R. 2020. A Review on Various Clustering Approaches for Image Segmentation. Proceedings of the Fourth International Conference on Inventive Systems and Control (ICISC 2020) IEEE Xplore Part Number: CFP20J06-ART; ISBN: 978-1-7281-2813-9.
  10. Shukla, S., Naganna, S. 2014. A Review on K-Means Data Clustering Approach. International Journal of Information & Computation Technology, vol 4, number 17, pp. 1847–1860, ISSN 0974-2239.
  11. Aneja, D., Rawat, T.K. 2013. Fuzzy Clustering Algorithms for Effective Medical Image Segmentation. International Journal of Intelligent Systems and Applications, 11, 55–61.
  12. Suganya, R., and Shanthi, R. 2012. Fuzzy C- Means Algorithm - A Review. International Journal of Scientific and Research Publications, 2(11).
  13. Larabi-Marie-Sainte, S., Alskireen, R., and Alhalawani, S. 2021. Emerging applications of Bio-Inspired Algorithms in image Segmentation. Electronics, 10(24), 3116.
  14. Debelee, T. G., Schwenker, F., Rahimeto, S., and Yohannes, D. 2019. Evaluation of modified adaptive k-means segmentation algorithm. Computational Visual Media, 5(4):347–361.
  15. Febrinanto, F. G., Dewi, C., and Triwiratno, A. 2019. The implementation of K-Means algorithm as image segmenting method in identifying the citrus leaves disease. IOP Conference Series Earth and Environmental Science, 243:1–11.
  16. Basar, S., Ali, M., Ochoa-Ruiz, G., Zareei, M., Waheed, A., and Adnan, A. 2020. Unsupervised color image segmentation: A case of RGB histogram-based K-means clustering initialization. PLoS ONE, 15(10), e0240015.
  17. Kumar, J., Nanda, R., Rath, R. K., and Rao, G. T. 2020. Image Segmentation using K-means Clustering. International Journal of Advanced Science and Technology, 29(6S), 3700–3704.
  18. Islam, M. Z., Nahar, S., Islam, S. S., Islam, S., Mukherjee, A., and Ali, L. E. 2021. Customized K Means Clustering Based Color Image Segmentation Measuring PRI. pp. 1–4.
  19. Saifullah, S., Suryotomo, A. P., and Yuwono, B. 2021. Fish detection using morphological approach based-on k-means segmentation. Compiler, 10(1).
  20. Pei, Y., Wang, W., and Zhang, S. 2012. Basic Ant Colony Optimization. International Conference on Computer Science and Electronics Engineering.
  21. Li, C., Liu, L., Sun, X., Zhao, J., and Yin, J. 2019. Image segmentation based on fuzzy clustering with cellular automata and features weighting. EURASIP Journal on Image and Video Processing, 2019(1).
  22. Nayak, T. N., and Bhoi, N. 2024. Robust fuzzy C-Means clustering algorithm based on normal shrink and membership filtering for image segmentation. CLEI Electronic Journal, 27(1).
  23. Shang, R., Chen, C., Wang, G., Jiao, L., Okoth, M. A., and Stolkin, R. 2020. A thumbnail-based hierarchical fuzzy clustering algorithm for SAR image segmentation. Signal Processing, 171, 107518.
  24. Wang, C., Pedrycz, W., Li, Z., and Zhou, M. 2021. Residual-driven fuzzy C-Means clustering for image segmentation. IEEE/CAA Journal of Automatica Sinica, 8(4), 876–889.
  25. Mohammdian-Khoshnoud, M., Soltanian, A. R., Dehghan, A., and Farhadian, M. 2022. Optimization of fuzzy c-means (FCM) clustering in cytology image segmentation using the gray wolf algorithm. BMC Molecular and Cell Biology, 23(1), 2.
  26. Vikraman, B. P., and Afthab, J. 2023. Fuzzy C-Means Approach Optimized using Raindrop Algorithm for Image Segmentation. In Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciences, pp. 33–44.
  27. Ticala, C., Pintea, C., and Matei, O. 2021. Sensitive ANT algorithm for edge detection in medical images. Applied Sciences, 11(23), 11303.
  28. Ab Wahab, M. N., Nefti-Meziani, S., and Atyabi, A. 2015. A Comprehensive Review of Swarm Optimization Algorithms. PLoS ONE, 10(5), e0122827.
  29. Kaur, A., and Singh, M. D. 2012. An Overview of PSO- Based Approaches in Image Segmentation. International Journal of Engineering and Technology, 2(8), 1349–1357.
  30. Xu, Y., Fan, P., and Yuan, L. 2012. A simple and efficient artificial bee colony algorithm. Mathematical Problems in Engineering, 2013:1–9.
  31. Ma, M., Liang, J., Guo, M., Fan, Y., and Yin, Y. 2011. SAR image segmentation based on Artificial Bee Colony algorithm. Applied Soft Computing, 11:5205–5214.
  32. Sahib, M. A., Abdulnabi, A. R., and Mohammed, M. A. 2018. Improving bacterial foraging algorithm using non-uniform elimination-dispersal probability distribution. Alexandria Engineering Journal, 57(4), 3341–3349.
  33. Chouhan, S. S., Kaul, A., and Singh, U. P. 2019. Plants Leaf Segmentation using Bacterial Foraging Optimization algorithm. pages 1500–1505.
  34. Haldurai, L., Madhubala, T., and Rajalakshmi, R. 2016. A Study on Genetic Algorithm and its Applications. International Journal of Computer Sciences and Engineering, 4(10):139–143.
  35. Kale, A., and Jain, A. 2014. A Review: Image Segmentation Using Genetic Algorithm. International Journal of Scientific & Engineering Research, 5(2).
  36. Khanna, K., and Arora, S. M. 2016. Ant Colony Optimization towards Image Processing. Indian Journal of Science and Technology, 9(48).
  37. Lee, KY., Park, JB. 2006. Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages. In: 2006 IEEE PES Power Systems Conference and Exposiiton, Atlanta, GA, pp. 188–192.
  38. Asgari, M., Pirahansiah, F., Shahverdy, M., Fartash, M. 2017. Using an Ant Colony Optimization Algorithm for Image Edge Detection as a Threshold Segmentation for OCR System. Journal of Theoretical and Applied Information Technology, vol. 95, no. 21.
  39. Kumari, R., Gupta, N., and Kumar, N. 2019. Image Segmentation using Improved Genetic Algorithm. International Journal of Engineering and Advanced Technology, 9(1), 1784–1792.
  40. Dahiya, P., Kumar, A., Kumar, A., and Nahavandi, B. 2022. Modified Artificial Bee Colony Algorithm-Based Strategy for Brain Tumor Segmentation. Computational Intelligence and Neuroscience, 2022:1–13.
  41. Su, H., Zhao, D., Yu, F., Heidari, A. A., Zhang, Y., Chen, H., Quan, S. 2022. Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images. Computers in Biology and Medicine, 142, 105181.
  42. Holland, J., Djenouri, Y., Laidi, R., and Yazidi, A. 2023. Hybrid Genetic U-Net Algorithm for medical segmentation. Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 558–564.
  43. Murinto, Prahara, A., and Ujianto, E. I. H. 2023. An Improved Segmentation Technique of Multispectral Image Using Modified Particle Swarm Optimization Algorithm. Int. J. Advance Soft Compu. Appl, 15(2).
  44. Huang, T., Yin, H., and Huang, X. 2024. Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation. Scientific Reports, 14(1).
  45. Wang, X. and Zhang, J. 2024. Image segmentation technology based on Ant Colony algorithm. J. Electrical Systems, 20(7s):1038–1042.
  46. El-Khatib, S., Skobtsov, Y., and Rodzin, S. 2019. Theoretical and experimental evaluation of hybrid ACO-K-Means image segmentation algorithm for MRI images using drift-analysis. Procedia Computer Science, 150, 324–332.
  47. Dhanachandra, N., and Chanu, Y. J. 2020. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Multimedia Tools and Applications, 79(25?26), 18839–18858.
  48. Zhi, H., and Liu, S. 2020. Gray image segmentation based on fuzzy c-means and artificial bee colony optimization. Journal of Intelligent & Fuzzy Systems, 38(4), 3647–3655.
  49. Lu, M., Xu, B., Qin, W., and Shi, J. 2020. Hybrid Ant Colony Optimization-Based Method for focal of a disease segmentation in lung CT images. In Lecture notes in computer science, pp. 215–222.
  50. Verma, H., Verma, D., and Tiwari, P. K. 2020. A population-based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image. Expert Systems with Applications, 167, 114121.
  51. Kulkarni, J. S. 2022. Image fusion by a hybrid multiobjective genetic algorithm technique. International Journal of Recent Technology and Engineering (IJRTE), 11(1), 123–128.
  52. Kanadath, A., Jothi, J. a. A., and Urolagin, S. 2023. Multilevel colonoscopy histopathology image segmentation using particle swarm optimization techniques. SN Computer Science, 4(5).
  53. Sabha, M., Thaher, T., and Emam, M. M. 2023. Cooperative swarm Intelligence algorithms for adaptive multilevel thresholding segmentation of COVID-19 CT-Scan images. JUCS - Journal of Universal Computer Science, 29(7), 759–804.
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Clustering Evolutionary Computation Segmentation Techniques Performance Metrics

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