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Multi-threshold segmentation of breast cancer images based on improved dandelion optimization algorithm

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Abstract

In the context of complex structures and blurred cell boundaries present in breast cancer histopathological tissue images under a microscope, traditional thresholding methods struggle to accurately separate lesion areas in breast cancer image segmentation. To address this challenge, we propose a multi-threshold segmentation method for breast cancer images based on an improved Dandelion Optimization algorithm. This approach incorporates the concept of opposite-based learning and utilizes the improved Dandelion Optimization algorithm to calculate the maximum between-class variance as the optimization objective. Moreover, the method establishes fallback strategies and incorporates a memory matrix, while leveraging the golden jackal energy judgment mechanism to identify optimal thresholds. The experimental results show that compared with the Crow search algorithm, Harris Hawks optimization algorithm, artificial gorilla troop optimization algorithm, dandelion optimization algorithm, ocean predator algorithm, whale optimization algorithm, sparrow search algorithm, and sine cosine algorithm, and the improved Dandelion optimization algorithm achieves the highest fitness value and converges at the fastest speed when using the same threshold number, it also occupies an advantageous position in terms of peak signal-to-noise ratio, structural similarity index, feature similarity index, and mean square error.

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The dataset used in this study can be accessed through the website https://databiox.com/.

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel Rebecca L, Torre Lindsey A, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6).

  2. Shah Shahid M, Khan Rizwan A, Arif S, Sajid U (2022) Artificial intelligence for breast cancer analysis: trends & directions. Comput Biol Med, 142.

  3. Yang X, Wang R, Zhao D, Yu F, Heidari Ali A, Xu Z, Chen H, Algarni Abeer D, Elmannai H, Xu S (2023) Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution. Biomed Signal Process Control 80(P2).

  4. Sun Y, Yang Y (2022) An adaptive bi-mutation-based differential evolution algorithm for multi-threshold image segmentation. Appl Sci 12(11).

  5. Fan W, Xu R, Yin Q, Yu W, Zhou J (2022) Application-driven graph partitioning. VLDB J 32(1).

  6. Mapurisa W, Sithole G (2022) Improved edge detection for satellite images. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022, V-2–2022.

  7. Liu X, Song L, Liu S, Zhang Y (2021) A review of deep-learning-based medical image segmentation methods. Sustainability 13(3).

  8. Pankaj U, Jitender Kumar C (2019) Kapur’s entropy based optimal multilevel image segmentation using Crow Search Algorithm. Appl Soft Comput J 97(PB).

  9. Rai R, Das A, Dhal KG (2022) Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. Evolving Syst 13(6).

  10. Ning G (2022) Two-dimensional Otsu multi-threshold image segmentation based on hybrid whale optimization algorithm. Multimedia Tools Appl 82(10).

  11. Ali Asghar H, Seyedali M, Hossam F, Ibrahim A, Majdi M, Huiling C (2019) Harris hawks optimization: algorithm and applications. Future Generation Comp Syst, 97.

  12. Pare S, Kumar A, Bajaj V, Singh GK (2016) A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Appl Soft Comp, 47.

  13. Wu D, Yuan C (2022) Threshold image segmentation based on improved sparrow search algorithm. Multimedia Tools Appl 81(23).

  14. Houssein Essam H, Abdelkareem Doaa A, Emam Marwa M, Hameed Mohamed A, Younan M (2022) An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm. Comp Biol Med, 149.

  15. Lan K, Zhou J, Jiang X, Wang J, Huang S, Yang J, Song Q, Tang R, Gong X, Liu K, Wu Y, Li T. Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation. Quantitative Imaging In Medicine And Surgery, 2023, 13(3).

  16. Zheng J, Gao Y, Zhang H, Lei Y, Zhang J (2022) OTSU multi-threshold image segmentation based on improved particle swarm algorithm. Appl Sci 12(22).

  17. Taymaz Rahkar F, Ahad KA (2020) A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimedia Syst (prepublish).

  18. Wang J, Zhu L, Wu B, Ryspayev A (2022) Forestry canopy image segmentation based on improved tuna swarm optimization. Forests 13(11).

  19. Vijh S, Saraswat M, Kumar S (2022) Automatic multilevel image thresholding segmentation using hybrid bio-inspired algorithm and artificial neural network for histopathology images. Multimedia Tools Appl 82(4).

  20. Agrawal S, Panda R, Choudhury P, Abraham A (2022) Dominant color component and adaptive whale optimization algorithm for multilevel thresholding of color images. Knowledge-Based Syst, 240.

  21. Sharma A, Chaturvedi R, Bhargava A (2022) A novel opposition based improved firefly algorithm for multilevel image segmentation. Multimedia Tools Appl 81(11).

  22. Xing Z, He Y (2021) Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm. Appl Soft Comput J 113(PA).

  23. Li K, Bai L, Li Y et al (2021) Improved Otsu Multi-Threshold Image Segmentation Method based on Sailfish Optimization. In: 33rd Chinese Control and Decision Conference (CCDC), pp 497–502. https://doi.org/10.26914/c.cnkihy.2021.028239.

  24. Jun Q, Xuanjing S, Fang M, Zheng F (2019) An Otsu multi-thresholds segmentation algorithm based on improved ACO. J Supercomput 75(2).

  25. Zhao S, Zhang T, Ma S, Chen M (2022) Dandelion optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Eng Appl Artif Intell, 114.

  26. Otsu N (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Trans Syst Man Cybern 9(1).

  27. Jumiawi WAH, Ali E (2022) Improvement in the between-class variance based on lognormal distribution for accurate image segmentation. Entropy 24(9).

  28. Jumiawi WAH, El-Zaart A (2022) Otsu Thresholding Model Using Heterogeneous Mean Filters for Precise Images Segmentation. In: International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE). M’sila, Algeria, pp 1–6. https://doi.org/10.1109/ICATEEE57445.2022.10093097

  29. Xiaofeng Q, Jiajun W, Xiaoling W et al (2023) Fast detection of dam zone boundary based on Otsu thresholding optimized by enhanced harris hawks optimization.. PloS one 18(2).

  30. Tizhoosh HR (2005) Opposition-Based Learning: A New Scheme for Machine Intelligence. In: International Conference on International Conference on Computational Intelligence for Modelling, Control & Automation. IEEE, 2005, pp 695–701.

  31. Mohamed Abd E, Ahmed A. Ewees, Diego O (2020) Hyper-heuristic method for multilevel thresholding image segmentation. Expert Syst Appl 146(C).

  32. Oliva D, Esquivel-Torres S, Hinojosa S, Pérez-Cisneros M, Osuna-Enciso V, Ortega-Sánchez N, Dhiman G, Heidari Ali A (2021) Opposition-based moth swarm algorithm. Expert Syst Appl, 184 p.

  33. Xu W, Zhang R, Chen L (2021) An improved crow search algorithm based on oppositional forgetting learning. Appl Intell 52(7).

  34. Yu X, Xu WY, Li CL (2021) Opposition-based learning grey wolf optimizer for global optimization. Knowledge-Based Syst, 226 p.

  35. N. Mittal, U. Singh, B.S. Sohi (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput, pp 1–16.

  36. Benyamin A, Farhad SG, Seyedali M (2021) Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36(10).

  37. Xu W, Li C, Dou Y, Zhang M, Dong Z, Jia D, Ban X (2021) Optimizing the weights and thresholds in dendritic neuron model using the whale optimization algorithm. J Phys Conf Ser 2025(1).

  38. Expert Systems; New Expert Systems Study Findings Have Been Reported by Researchers at Illinois Institute of Technology (Marine Predators Algorithm: a Nature-inspired Metaheuristic). Journal of Robotics & Machine Learning, 2020.

  39. Mirjalili S (2016) SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Syst, 96 p.

  40. Hamidreza B, Elham A, Maryam T, Somayyeh JJ (2020) A histopathological image dataset for grading breast invasive ductal carcinomas. Inform Med Unlocked, 19 (prepublish).

  41. Kezong T, Xuan X, Jun W, Jingyu Y, Limin L (2017) An improved multilevel thresholding approach based modified bacterial foraging optimization. Appl Intell 46(1).

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Funding

This research is supported by the Natural Science Foundation of Jilin Provincial Department of Science and Technology (YDZJ202201ZYTS558), Jilin Provincial Department of Science and Technology project (YDZJ202201ZYTS605), Jilin Provincial Education Science Planning General Project (GH20276), and Jilin Provincial Key Research and Development Project (Project No: 20200404223YY).

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Authors and Affiliations

  1. College of Computer Science and Technology, Bei Hua University, Jilin, Jilin, 130032, China

    Zhenghong Wang, Fanhua Yu, Dan Wang, Taihui Liu & Rongjun Hu

Authors
  1. Zhenghong Wang
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  2. Fanhua Yu
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  3. Dan Wang
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  4. Taihui Liu
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Contributions

ZW designed and developed the enhanced Dandelion Optimization algorithm, proposed the multi-level threshold segmentation approach, conducted the algorithm evaluation, and contributed to manuscript writing. FY and DW provided academic guidance, assisted in research direction determination, and contributed to manuscript writing and revision. RH conducted experimental verification, performance evaluation, result visualization, and manuscript editing. TL assisted in manuscript proofreading, image editing, and overall refinement.

Corresponding author

Correspondence to Fanhua Yu.

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Conflict of interest

The authors declare no competing interests, financial or personal, in the research, data interpretation, and manuscript writing.

Ethical Approval

1. The dataset used in this study is sourced from the website https://databiox.com/. 2. To the best of our knowledge, the website has obtained the necessary ethical approvals for data collection and sharing. 3. We have adhered to the ethical standards and policies specified by the website during the data usage process.

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Wang, Z., Yu, F., Wang, D. et al. Multi-threshold segmentation of breast cancer images based on improved dandelion optimization algorithm. J Supercomput 80, 3849–3874 (2024). https://doi.org/10.1007/s11227-023-05605-5

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