AGPLO-Driven Optimisation for Accurate Segmentation of Papillary Thyroid Carcinoma in Medical Imaging

Abstract

Papillary Thyroid Carcinoma (PTC) is the most prevalent thyroid malignancy, and accurate lesion segmentation is essential for clinical diagnosis and treatment planning. Metaheuristic optimisation algorithms have been widely used in Multi-Threshold Image Segmentation (MTIS), but many existing methods suffer from an imbalance between global exploration and local exploitation. This study aims to develop a robust and well-balanced optimisation algorithm to improve the accuracy and stability of MTIS for PTC images. An Adaptive Guided Polar Lights Optimisation (AGPLO) algorithm is proposed, which incorporates an adaptive phase-shift operator, magnetic guiding convergence, and energy burst exploration mechanisms to dynamically regulate search behaviour. AGPLO was evaluated on the IEEE CEC2017 benchmark suite and applied to Rényi entropy-based MTIS for PTC image segmentation. Experimental results on benchmark functions demonstrate that AGPLO outperforms several original and advanced metaheuristic algorithms in terms of convergence accuracy, stability, and robustness. In PTC image segmentation experiments, AGPLO achieves superior PSNR, SSIM, and FSIM values, producing clearer lesion boundaries and preserving structural details more effectively than comparative methods. The proposed AGPLO provides an effective and reliable optimisation framework for MTIS and shows strong potential for intelligent medical image analysis applications.

Keywords

AGPLO / medical image segmentation / metaheuristic algorithms / papillary thyroid carcinoma

Funding

This work was supported by Zhejiang Province Natural Science Foundation (Grant LTGY24H050004) (YPST), (Grant LTGY23H050003) (YZ) and the Wenzhou Municipal Science and Technology Bureau of China (Grant Y2023065) (YPST). This work was also supported by the National Natural Science Foundation of China (Grants U25A20450, 62571374).

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data available on request from the authors.

References

[1] F. Cherifi and A. Awada, “Molecular Oncology of Iodine Refractory Thyroid Cancer Current Therapies and Perspective,” Critical Reviews in Oncology 209, (2025): 104679, https://doi.org/10.1016/j.critrevonc.2025.104679.

[2] N. Ren, X. Shang, G. Wu, and X. Tian, “Papillary Thyroid Carcinoma With Parapharyngeal and Pulmonary Metastases: A Case Report and Literature Review,” Asian Journal of Surgery 47, no. 4 (2024): 1917-1918, https://doi.org/10.1016/j.asjsur.2023.12.129.

[3] S. Weller, C. Chu, and A. K.-Y. Lam, “Assessing the Rise in Papillary Thyroid Cancer Incidence: A 38-Year Australian Study Investigating Who Classification Influence,” Journal of Epidemiology and Global Health 15, no. 1 (2025): 9, https://doi.org/10.1007/s44197-025-00354-5.

[4] R. Cao, X. Li, W. Chen, et al., An Adaptive Conjugate Gradient Accelerated Evolutionary Algorithm for Multi-Objective Spot Optimization in Cancer Intensity Modulated Proton Therapy,” Applied Soft Computing 151 (2024): 111177, https://doi.org/10.1016/j.asoc.2023.111177.

[5] M. J. Ali, M. Essaid, L. Moalic, and L. Idoumghar, “A Review of Automl Optimization Techniques for Medical Image Applications,” Computerized Medical Imaging and Graphics 118 (2024): 102441, https://doi.org/10.1016/j.compmedimag.2024.102441.

[6] N. Halvorsen, I. Barua, S.-E. Kudo, et al., Leaving Colorectal Polyps in Situ With Endocytoscopy Assisted by Computer-Aided Diagnosis: A Cost-Effectiveness Study,” Endoscopy 57, no. 06 (2025): 611-619.

[7] Y. Xu, R. Quan, W. Xu, Y. Huang, X. Chen, and F. Liu, “Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches,” Bioengineering-Basel 11, no. 10 (2024): 1034, https://doi.org/10.3390/bioengineering11101034.

[8] L. Zhang, H. Niu, P. Liu, F. Wen, and R. Ying, “Efficient Plane Segmentation in Depth Image Based on Adaptive Patch-Wise Region Growing,” IEEE Robotics and Automation Letters (2025).

[9] L. Xiao, B. Zhou, and C. Fan, “Automatic Brain Mri Tumors Segmentation Based on Deep Fusion of Weak Edge and Context Features,” Artificial Intelligence Review 58, no. 5 (2025): 154, https://doi.org/10.1007/s10462-025-11151-8.

[10] T. Payatsuporn, P. Kantavat, N. Tangnuntachai, et al., Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders,” IEEE Access 13 (2025): 17340-17353, https://doi.org/10.1109/ACCESS.2025.3532505.

[11] Y. Yuan, S. Hou, X. Wu, et al., Application of Deep-Learning to the Automatic Segmentation and Classification of Lateral Lymph Nodes on Ultrasound Images of Papillary Thyroid Carcinoma,” Asian Journal of Surgery 47, no. 9 (2024): 3892-3898, https://doi.org/10.1016/j.asjsur.2024.02.140.

[12] V. L’Imperio, V. Coelho, G. Cazzaniga, et al., Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a Nutshell,” Modern Pathology 37, no. 12 (2024): 100608, https://doi.org/10.1016/j.modpat.2024.100608.

[13] D. Das, M. S. Iyengar, M. S. Majdi, J. J. Rodriguez, and M. Alsayed, “Deep Learning for Thyroid Nodule Examination: A Technical Review,” Artificial Intelligence Review 57, no. 3 (2024): 47, https://doi.org/10.1007/s10462-023-10635-9.

[14] J. Ni, Y. You, X. Wu, X. Chen, J. Wang, and Y. Li, “Performance Evaluation of Deep Learning for the Detection and Segmentation of Thyroid Nodules: Systematic Review and Meta-Analysis,” Journal of Medical Internet Research 27 (2025): e73516, https://doi.org/10.2196/73516.

[15] S. Xu, W. Jiang, Y. Chen, et al., Rebsa: Enhanced Backtracking Search for Multi-Threshold Segmentation of Breast Cancer Images,” Biomedical Signal Processing and Control 106 (2025): 107733, https://doi.org/10.1016/j.bspc.2025.107733.

[16] J. Zhan, “Quantum Feasibility Labeling for np-Complete Vertex Coloring Problem,” IEEE Access (2025).

[17] R. Storn and K. Price, “Differential Evolution-A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces,” Journal of Global Optimization 11, no. 4 (1997): 341-359, https://doi.org/10.1023/a:1008202821328.

[18] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Proceedings of ICNN’95-international Conference on Neural Networks, Vol. 4 (IEEE, 1995), 1942-1948, https://doi.org/10.1109/icnn.1995.488968.

[19] S. Li, H. Chen, M. Wang, A. A. Heidari, and S. Mirjalili, “Slime Mould Algorithm: A New Method for Stochastic Optimization,” Future Generation Computer Systems 111 (2020): 300-323, https://doi.org/10.1016/j.future.2020.03.055.

[20] H. Su, D. Zhao, A. A. Heidari, et al., Rime: A Physics-Based Optimization,” Neurocomputing 532 (2023): 183-214, https://doi.org/10.1016/j.neucom.2023.02.010.

[21] C. Yuan, D. Zhao, A. A. Heidari, L. Liu, Y. Chen, and H. Chen, “Polar Lights Optimizer: Algorithm and Applications in Image Segmentation and Feature Selection,” Neurocomputing 607 (2024): 128427, https://doi.org/10.1016/j.neucom.2024.128427.

[22] G. Wu, R. Mallipeddi, and P. N. Suganthan , Problem Definitions and Evaluation Criteria for the Cec 2017 Competition on Constrained Real-Parameter Optimization. Technical Report (National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore).

[23] B. Zheng, Y. Chen, C. Wang, A. A. Heidari, L. Liu, and H. Chen, “The Moss Growth Optimization (Mgo): Concepts and Performance,” Journal of Computational Design and Engineering 11, no. 5 (2024): 184-221, https://doi.org/10.1093/jcde/qwae080.

[24] C. Yuan, D. Zhao, A. A. Heidari, et al., Artemisinin Optimization Based on Malaria Therapy: Algorithm and Applications to Medical Image Segmentation,” Displays 84 (2024): 102740, https://doi.org/10.1016/j.displa.2024.102740.

[25] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris Hawks Optimization: Algorithm and Applications,” Future Generation Computer Systems 97 (2019): 849-872, https://doi.org/10.1016/j.future.2019.02.028.

[26] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software 69 (2014): 46-61, https://doi.org/10.1016/j.advengsoft.2013.12.007.

[27] S. Mirjalili, “Sca: A Sine Cosine Algorithm for Solving Optimization Problems,” Knowledge-Based Systems 96 (2016): 120-133, https://doi.org/10.1016/j.knosys.2015.12.022.

[28] Y. Cao, H. Zhang, W. Li, M. Zhou, Y. Zhang, and W. A. Chaovlitwongse, “Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions,” IEEE Transactions on Evolutionary Computation 23, no. 4 (2018): 718-731, https://doi.org/10.1109/tevc.2018.2885075.

[29] D. Jia, G. Zheng, B. Qu, and M. K. Khan, “A Hybrid Particle Swarm Optimization Algorithm for High-Dimensional Problems,” Computers & Industrial Engineering 61, no. 4 (2011): 1117-1122, https://doi.org/10.1016/j.cie.2011.06.024.

[30] M. Abd Elaziz, D. Oliva, and S. Xiong, “An Improved Opposition-Based Sine Cosine Algorithm for Global Optimization,” Expert Systems with Applications 90 (2017): 484-500, https://doi.org/10.1016/j.eswa.2017.07.043.

[31] M. A. Al-Betar, M. A. Awadallah, H. Faris, I. Aljarah, and A. I. Hammouri, “Natural Selection Methods for Grey Wolf Optimizer,” Expert Systems With Applications 113 (2018): 481-498, https://doi.org/10.1016/j.eswa.2018.07.022.

[32] L. Peng, C. He, A. A. Heidari, et al., Information Sharing Search Boosted Whale Optimizer With Nelder-Mead Simplex for Parameter Estimation of Photovoltaic Models,” Energy Conversion and Management 270 (2022): 116246, https://doi.org/10.1016/j.enconman.2022.116246.

[33] Y. Ling, Y. Zhou, and Q. Luo, “Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization,” IEEE Access 5 (2017): 6168-6186, https://doi.org/10.1109/access.2017.2695498.

[34] H. Liang, Y. Liu, Y. Shen, F. Li, and Y. Man, “A Hybrid Bat Algorithm for Economic Dispatch With Random Wind Power,” IEEE Transactions on Power Systems 33, no. 5 (2018): 5052-5061, https://doi.org/10.1109/tpwrs.2018.2812711.

[35] W.-N. Chen, J. Zhang, Y. Lin, et al., Particle Swarm Optimization With an Aging Leader and Challengers,” IEEE Transactions on Evolutionary Computation 17, no. 2 (2012): 241-258, https://doi.org/10.1109/tevc.2011.2173577.

[36] D. Zhao, A. Qi, F. Yu, A. A. Heidari, H. Chen, and Y. Li, “Multi-Strategy Ant Colony Optimization for Multi-Level Image Segmentation: Case Study of Melanoma,” Biomedical Signal Processing and Control 83 (2023): 104647, https://doi.org/10.1016/j.bspc.2023.104647.

[37] J. Shi, Y. Chen, A. A. Heidari, et al., Environment Random Interaction of Rime Optimization With Nelder-Mead Simplex for Parameter Estimation of Photovoltaic Models,” Scientific Reports 14, no. 1 (2024): 15701, https://doi.org/10.1038/s41598-024-65292-x.

[38] P. Civicioglu, “Backtracking Search Optimization Algorithm for Numerical Optimization Problems,” Applied Mathematics and Computation 219, no. 15 (2013): 8121-8144, https://doi.org/10.1016/j.amc.2013.02.017.

[39] J. Ma, T. Ting, K. L. Man, et al., Parameter Estimation of Photovoltaic Models via Cuckoo Search,” Journal of Applied Mathematics (2013).

[40] S. Mirjalili, “Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm,” Knowledge-Based Systems 89 (2015): 228-249, https://doi.org/10.1016/j.knosys.2015.07.006.

[41] L. Ren, A. A. Heidari, Z. Cai, et al., Gaussian Kernel Probability-Driven Slime Mould Algorithm With New Movement Mechanism for Multi-Level Image Segmentation,” Measurement 192 (2022): 110884, https://doi.org/10.1016/j.measurement.2022.110884.

[42] Z. Cai, J. Gu, J. Luo, et al., Evolving an Optimal Kernel Extreme Learning Machine by Using an Enhanced Grey Wolf Optimization Strategy,” Expert Systems With Applications 138 (2019): 112814, https://doi.org/10.1016/j.eswa.2019.07.031.

[43] F. Qiu, R. Guo, H. Chen, and G. Liang, “Boosting Slime Mould Algorithm for High-Dimensional Gene Data Mining: Diversity Analysis and Feature Selection,” Computational and Mathematical Methods in Medicine 2022, no. 1 (2022): 8011003-8011027, https://doi.org/10.1155/2022/8011003.