Enhancing Image Segmentation with Darwinian Grey Wolf Optimizer: A Novel Multilevel Thresholding Approach | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 2، تیر 2024، صفحه 217-225 اصل مقاله (1.38 M) | ||
| نوع مقاله: Applied Article | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.14469.2550 | ||
| نویسنده | ||
| Ehsan Ehsaeyan* | ||
| Electrical Engineering Department, Sirjan University of Technology, Sirjan, Iran. | ||
| چکیده | ||
| This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, which often results in local optima and premature convergence. The search agents are efficiently steered across the search space by a dual mechanism of encouragement and punishment employed by our strategy, thereby curtailing computational time. This is implemented by segmenting the population into distinct groups, each tasked with discovering superior solutions. To validate the algorithm’s efficacy, 9 test images from the Pascal VOC dataset were selected, and the renowned energy curve method was employed for verification. Additionally, Kapur entropy was utilized to gauge the algorithm’s performance. The method was benchmarked against four disparate search algorithms, and its dominance was underscored by achieving the best outcomes in 20 out of 27 cases for image segmentation. The experimental findings collectively affirm that the Darwinian Grey Wolf Optimizer (DGWO) stands as a formidable instrument for multilevel thresholding. | ||
| کلیدواژهها | ||
| Image thresholding؛ multilevel segmentation؛ Grey Wolf Optimizer؛ Darwinian rule | ||
| مراجع | ||
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[1] F. Fakouri, M. Nikpour, and A. Soleymani Amiri, “Automatic Brain Tumor Detection in Brain MRI Images Using Deep Learning Methods,” Journal of Ai and Data Mining, vol. 12, no. 1, pp. 27–35, Jan. 2024.
[2] L. F. Lyu and W. D. Zhu, “Operational Modal Analysis of a Rotating Structure Subject to Random Excitation Using a Tracking Continuously Scanning Laser Doppler Vibrometer via an Improved Demodulation Method,” Journal of Vibration and Acoustics, vol. 144, no. 1, Jun. 2021.
[3] L. He and S. Huang, “An efficient krill herd algorithm for color image multilevel thresholding segmentation problem,” Applied Soft Computing, vol. 89, p. 106063, Apr. 2020.
[4] Z. Xing and H. Jia, “Modified thermal exchange optimization based multilevel thresholding for color image segmentation,” Multimedia Tools and Applications, vol. 79, no. 1–2, pp. 1137–1168, Oct. 2019.
[5] S. J. Mousavirad and H. Ebrahimpour-Komleh, “Human mental search-based multilevel thresholding for image segmentation,” Applied Soft Computing, p. 105427, Apr. 2019.
[6] M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Systems with Applications, vol. 166, p. 113917, Mar. 2021.
[7] X. Zhang, Q. Lin, W. Mao, S. Liu, Z. Dou, and G. Liu, “Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization,” Applied Soft Computing, vol. 101, pp. 107061–107061, Mar. 2021.
[8] Erwin and T. Yuningsih, “Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 45, no. 2, pp. 435–446, Aug. 2020.
[9] A. K. M. Khairuzzaman and S. Chaudhury, “Multilevel thresholding using grey wolf optimizer for image segmentation,” Expert Systems with Applications, vol. 86, pp. 64–76, Nov. 2017.
[10] H. Song, J. Wang, J. Bei, and M. Wang, “Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases,” Expert Systems with Applications, pp. 124624–124624, Jun. 2024.
[11] Mohamed Abd Elaziz, Mohammed A.A. Al-qaness, Rehab Ali Ibrahim, A. A. Ewees, and Mansour Shrahili, “Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors,” Egyptian Informatics Journal, vol. 27, pp. 100500–100500, Sep. 2024.
[12] H. Guo et al., “Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images,” Computers in Biology and Medicine, vol. 168, pp. 107769–107769, Jan. 2024.
[13] J. Shi, Y. Chen, Z. Cai, Ali Asghar Heidari, H. Chen, and X. Chen, “Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis,” Biomedical Signal Processing and Control, vol. 96, pp. 106492–106492, Oct. 2024.
[14] J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer Vision, Graphics, and Image Processing, vol. 29, no. 3, pp. 273–285, Mar. 1985.
[15] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014.
[16] P. Ghamisi, M. S. Couceiro, J. A. Benediktsson, and N. M. F. Ferreira, “An efficient method for segmentation of images based on fractional calculus and natural selection,” Expert Systems with Applications, vol. 39, no. 16, pp. 12407–12417, Nov. 2012.
[17] M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes Challenge: A Retrospective,” International Journal of Computer Vision, vol. 111, no. 1, pp. 98–136, Jun. 2014.
[18] Lin Zhang, Lei Zhang, Xuanqin Mou, and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,” IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378–2386, Aug. 2011.
[19] kooaslansefat, “CEC 2017 Benchmark,” Kaggle.com, Mar. 07, 2023. https://www.kaggle.com/code/kooaslansefat/cec-2017-benchmark (accessed Aug. 27, 2024). | ||
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