Skip to main content

Advertisement

SpringerLink
Log in

Giza pyramids construction algorithm with gradient contour approach for multilevel thresholding color image segmentation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Segmentation is an essential processing step in computer vision. As a popular method in image segmentation, the pivotal of multilevel thresholding lies in determining specific thresholds of the image. In this paper, we introduce an enhanced Giza pyramids construction algorithm (GPC) with a gradient contour approach, termed GGPC, for solving color image segmentation problems. In the proposed GGPC algorithm, the gradient contour approach explores the fitness landscape based on population distributions to speculate promising summits, and provides an efficient guidance for further evolution. A composite suit of benchmark functions is adopted to evaluate the proposed GGPC algorithm which is shown via extensive comparisons to outperform eight state-of-the-art algorithms. To further develop the application potential, we propose an excellent GGPC-based thresholding segmentation method by multilevel Kapur entropy. The method is successfully exploited in color image segmentation with respect to different categories of benchmark images. Experiment results demonstrate that the GGPC-based segmentation method also exhibits better performance over peers, providing a more effective technique for color image segmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. He K, Gkioxari G, Dollar P et al (2020) Mask R CNN. IEEE T Pattern Anal 42:386–397. https://doi.org/10.1109/TPAMI.2018.2844175

    Google Scholar 

  2. Manzke R, Meyer C, Ecabert O et al (2010) Automatic segmentation of rotational X-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures. IEEE T Med Imaging 29 (2):260C272. https://doi.org/10.1109/TMI.2009.2021946

    Google Scholar 

  3. Yang Y, Tian D, Wu B (2018) A fast and reliable noise-resistant medical image segmentation and bias field correction model. Magn Reson Imaging 54:15–31. https://doi.org/10.1016/j.mri.2018.06.015

    Google Scholar 

  4. Tuan TM, Ngan TT, Son LH (2016) A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation. Appl Intell 45:402C428. https://doi.org/10.1007/s10489-016-0763-5

    Google Scholar 

  5. Zhao W, Lou M, Qi Y et al (2021) Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms. Appl Intell 51:8810C8827. https://doi.org/10.1007/s10489-021-02297-3

    Google Scholar 

  6. Kamel M, Zhao A (1993) Extraction of binary character/graphics images from grayscale document images. Models Image Process 55(3):203–217. https://doi.org/10.1006/gmip.1993.1015

    Google Scholar 

  7. Bhanu B (1986) Automatic target recognition: state of the art survey. IEEE T Aero Elec Sys 22:364–379. https://doi.org/10.1109/TAES.1986.310772

    Google Scholar 

  8. Sezgin M, Tasaltin R (2000) A new dichotomization technique to multilevel thresholding devoted to inspection applications. Pattern Recogn Lett 21:151–161. https://doi.org/10.1016/S0167-8655(99)00142-7

    Google Scholar 

  9. Zhenfeng S, Weixun Z, Xueqing D et al (2020) Multilabel remote sensing image retrieval based on fully convolutional network. IEEE J-Stars 13:318–328. https://doi.org/10.1109/JSTARS.2019.2961634

    Google Scholar 

  10. Farhat W, Sghaier H, Faiedh H et al (2019) Design of efficient embedded system for road sign recognition. J Ambient Intell Humaniz Comput 10:491–507. https://doi.org/10.1007/s12652-017-0673-3

    Google Scholar 

  11. Ahmadi SBB, Zhang G, Rabbani M et al (2020) An intelligent and blind dual color image watermarking for authentication and copyright protection. Appl Intell 51:1701C1732. https://doi.org/10.1007/s10489-020-01903-0

    Google Scholar 

  12. Xing ZK (2020) An improved emperor penguin optimization based multilevel thresholding for color image segmentation. Knowl-Based Syst 194:105570. https://doi.org/10.1016/j.knosys.2020.105570

    Google Scholar 

  13. Houssein EH, Neggaz N, Hosney ME, Mohamed WM, Hassaballah M (2021) Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities Neural Comput Appl 1C18. https://doi.org/10.1007/s00521-021-05991-y

  14. Hashim FA, Houssein EH, Hussain K et al (2020) A modified henry gas solubility optimization for solving motif discovery problem. Neural Comput Appl 32(14):10759C10771. https://doi.org/10.1007/s00521-019-04611-0

    Google Scholar 

  15. Satapathy SC, Raja NSM, Rajinikanth V et al (2018) Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput Appl 29(12):1285–1307. https://doi.org/10.1007/s00521-016-2645-5

    Google Scholar 

  16. Levinshtein A, Stere A, Kutulakos KN, et al. (2009) Turbopixels: fast superpixels using geometric flows. IEEE T Pattern Anal 31(12):2290–2297. https://doi.org/10.1007/10.1109/TPAMI.2009.96

    Google Scholar 

  17. He C, Li S, Xiong D (2020) Remote sensing image semantic segmentation based on edge information guidance. Remote Sens 12:1501. https://doi.org/10.3390/rs12091501

    Google Scholar 

  18. Keuper M, Tang S, Andres B et al (2020) Motion Segmentation and Multiple Object Tracking by Correlation Co-Clustering. IEEE T Pattern Anal 42:140–153. https://doi.org/10.1109/TPAMI.2018.2876253

    Google Scholar 

  19. Shao Z, Zhou W, Deng X, et al. (2020) Multilabel remote sensing image retrieval based on fully convolutional network. IEEE J-Stars 13:318–328. https://doi.org/10.1109/JSTARS.2019.2961634

    Google Scholar 

  20. Zhou Z, Siddiquee MMR, Tajbakhsh N, et al. (2020) UNet plus plus: redesigning skip connections to exploit multiscale features in image segmentation. IEEE T Med Imaging 42:140–153. https://doi.org/10.1109/TMI.2019.2959609

    Google Scholar 

  21. Stutz D, Hermans A, Leibe B (2018) Superpixels: an evaluation of the state-of-the-art. Comput Vis Image Und 166:1–27. https://doi.org/10.1016/j.cviu.2017.03.007

    Google Scholar 

  22. Ciecholewski M (2015) Automated coronal hole segmentation from Solar EUV images using the watershed transform. J Vis Commun Image R 33:203–218. https://doi.org/10.1016/j.jvcir.2015.09.015

    Google Scholar 

  23. Cousty J, Bertrand G, Najman L et al (2010) Watershed cuts: thinnings, shortest path forests, and topological watersheds. IEEE T Pattern Anal 32:925C939. https://doi.org/10.1109/TPAMI.2009.71

    Google Scholar 

  24. Breve F (2019) Interactive image segmentation using label propagation through complex networks. Expert Syst Appl 123:18–33. https://doi.org/10.1016/j.eswa.2019.01.031

    Google Scholar 

  25. Lang C, Jia H (2019) Kapurs entropy for color image segmentation based on a hybrid whale optimization algorithm. Entropy-Switz 123:18–33. https://doi.org/10.3390/e21030318

    Google Scholar 

  26. Zhao D, Liu L, Yu F, et al. (2021) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl-Based Syst 216:106510. https://doi.org/10.1016/j.knosys.2020.106510

    Google Scholar 

  27. Back AD, Angus D, Wiles J (2020) Transitive entropy-a rank ordered approach for natural sequences. IEEE J-STSP 14:312–321. https://doi.org/10.1109/JSTSP.2019.2939998

    Google Scholar 

  28. Wu C, Cao Z (2021) Entropy-like divergence based kernel fuzzy clustering for robust image segmentation. Expert Syst Appl 169:114327. https://doi.org/10.1016/j.eswa.2020.114327

    Google Scholar 

  29. Oliveira RB, Papa JP, Pereira AS (2018) Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput Appl 29(3):613–636. https://doi.org/10.1007/s00521-016-2482-6

    Google Scholar 

  30. Gupta S, Deep K (2020) Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation. Neural Comput Appl 32(13):9521C9543. https://doi.org/10.1007/s00521-019-04465-6

    Google Scholar 

  31. Abdel-Basset M, Chang V, Mohamed R (2020) A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems. Neural Comput Appl 1:1C34. https://doi.org/10.1007/s00521-020-04820-y

    Google Scholar 

  32. Dhal KG, Das A, Rag S, et al. (2019) Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation. Arch Comput Method E 27(3):855–888. https://doi.org/10.1007/s11831-019-09334-y

    MathSciNet  Google Scholar 

  33. Pare S, Kumar A, Bajaj V (2017) An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy. Appl Soft Comput 61:570–592. https://doi.org/10.1016/j.asoc.2017.08.039

    Google Scholar 

  34. Zhao D, Liu L, Yu F, Heidari AA, et al. (2020) Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst Appl 167:14122. https://doi.org/10.1016/j.eswa.2020.114122

    Google Scholar 

  35. Rodriguez-Esparza E, Zanella-Calzada LA, Oliva D et al (2020) An efficient Harris hawks-inspired image segmentation method. Expert Syst Appl 155:113428. https://doi.org/10.1016/j.eswa.2020.113428

    Google Scholar 

  36. Mirghasemi S, Yazdi HS, Lotfizad M (2012) A target-based color space for sea target detection. Appl Intell 36:960C978. https://doi.org/10.1007/s10489-011-0307-y

    Google Scholar 

  37. Yang Z, Angus W (2020) A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation. Neural Comput Appl 32(16):12011C12031. https://doi.org/10.1007/s00521-019-04210-z

    Google Scholar 

  38. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

    Google Scholar 

  39. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Google Scholar 

  40. Jia H, Peng X, Song W et al (2019) Multiverse optimization algorithm based on levy flight improvement for multithreshold color image segmentation. IEEE Access 7:32805–32844. https://doi.org/10.1109/ACCESS.2019.2903345

    Google Scholar 

  41. Wei D, Wang Z, Si L et al (2021) Preaching-inspired swarm intelligence algorithm and its applications. Knowl-Based Syst 211:106552. https://doi.org/10.1016/j.knosys.2020.106552

    Google Scholar 

  42. Gupta S, Deep K (2019) Improved sine cosine algorithm with crossover scheme for global optimization. Knowl-Based Syst 165:374–406. https://doi.org/10.1016/j.knosys.2018.12.008

    Google Scholar 

  43. Aziz MAE, Ewees AA, Hassanien AE (2017) Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256. https://doi.org/10.1016/j.eswa.2017.04.023

    Google Scholar 

  44. Pan Y, Xia Y, Zhou T et al (2017) Cell image segmentation using bacterial foraging optimization. Appl Soft Comput 58:770C782. https://doi.org/10.1016/j.asoc.2017.05.019

    Google Scholar 

  45. Singh S, Mittal N, Singh H (2020) A multilevel thresholding algorithm using LebTLBO for image segmentation. Neural Comput Appl 32:16681C16706. https://doi.org/10.1007/s00521-020-04989-2

    Google Scholar 

  46. Ashish KB (2020) A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Comput Appl 32(9):4583–4613. https://doi.org/10.1007/s00521-018-3771-z

    Google Scholar 

  47. Omar A, Ernesto A, Fernando W, Marco PC (1005) An accurate Cluster chaotic optimization approach for digital medical image segmentation. Neural Comput Appl 33:10057C10091. https://doi.org/10.1007/s00521-021-05771-8

    Google Scholar 

  48. Sun Y, Yen GG, Yi Z (2018) Evolving unsupervised deep neural networks for learning meaningful representations. IEEE T Evolut Comput 23(1):89–103. https://doi.org/10.1109/TEVC.2018.2808689

    Google Scholar 

  49. Omidvar M, Li X, Yao X (2021) A review of population-based metaheuristics for large-scale black-box global optimization: Part B. IEEE T Evolut Comput 26(5):823–843. https://doi.org/10.1109/TEVC.2021.3130835

    Google Scholar 

  50. Woo DK, Choi JH, Ali M, et al. (2011) A novel multimodal optimization algorithm applied to electromagnetic optimization. IEEE T Magn 47(6):1667–1673. https://doi.org/10.1109/TMAG.2011.2106218

    Google Scholar 

  51. Zheng Y, Du Y, Ling H et al (2019) Evolutionary collaborative human-UAV search for escaped criminals. IEEE T Evolut Comput 24(2):217–231. https://doi.org/10.1109/TEVC.2019.2925175

    Google Scholar 

  52. Zaman F, Elsayed SM, Ray T, et al. (2017) Evolutionary algorithms for finding Nash equilibria in electricity markets. IEEE T Evolut Comput 22(4):536–549. https://doi.org/10.1109/TEVC.2017.2742502

    Google Scholar 

  53. Harifi S, Mohammadzadeh J, Khalilian M, et al. (2021) Giza pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization. Evol Comput 4(14):1743–1761. https://doi.org/10.1007/s12065-020-00451-3

    Google Scholar 

  54. Song B, Wang Z, Zou L (2017) On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cogn Comput 9:5–17. https://doi.org/10.1007/s12559-016-9442-4

    Google Scholar 

  55. Lin Y, Zhang J, Lan L (2008) A contour method in population-based stochastic algorithms. In: Proceedings of the 2008 IEEE congress on evolutionary computation. IEEE, pp 2388–2395. https://doi.org/10.1109/CEC.2008.4631117

  56. Wang Z, Zhan Z, Lin Y et al (2020) Automatic Niching differential evolution with contour prediction approach for Multimodal optimization problems. IEEE T Evolut Comput 1(24):124–128. https://doi.org/10.1109/TEVC.2019.2910721

    Google Scholar 

  57. Zhang MJ, Smart W (2004) Genetic programming with gradient descent search for multiclass object classification. In: Proceedings of the 7th European conference on genetic programming. Springer, pp 399–408. https://doi.org/10.1007/978-3-540-24650-338

  58. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    MathSciNet  MATH  Google Scholar 

  59. Otsu NA (2007) Threshold selection method from gray-level histograms. IEEE T Syst Man Cy-S 1(9):62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Google Scholar 

  60. Bandopadhyay R, Kundu R, Oliva D (2021) Segmentation of brain mri using an altruistic harris hawks optimization algorithm. Knowl-Based Syst 232:107468. https://doi.org/10.1016/j.knosys.2021.107468

    Google Scholar 

  61. Levine MD, Nazif AM (1985) Dynamic measurement of computer generated image segmentations. IEEE T Pattern Anal 7(2):155–164. https://doi.org/10.1109/tpami.1985.4767640

    Google Scholar 

  62. Sahoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Graph Models 41:233–260. https://doi.org/10.1016/0734-189X(88)90022-9

    Google Scholar 

  63. Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Und 2(110):260–280. https://doi.org/10.1016/j.cviu.2007.08.003

    Google Scholar 

  64. Rosenberger C, Chabrier S, Laurent H, Emile B (2006) Unsupervised and supervised image segmentation evaluation. In: Zhang YJ (ed) Advances in image and video segmentation, IRM Press: Pennsylvania, USA, vol 18, pp 365C393

  65. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of histogram. Graph Models 29(1):273–285. https://doi.org/10.1016/S0734-189X(85)90156-2

    Google Scholar 

  66. Wu Y, Ji S (2010) Multi threshold selection for an image based on gray entropy and chaotic particle swarm optimization. CAAI Transactions on Intelligence Systems 5(6):522–529. https://doi.org/10.3969/j.issn.1673-4785.2010.06.009

    Google Scholar 

  67. Li CH, Lee CK (1996) Minimum cross entropy thresholding. Pattern Recogn 29(4):575–580. https://doi.org/10.1016/0031-3203(93)90115-D

    Google Scholar 

  68. Bergh F, Engelbrecht AP (2005) A study of particle swarm optimization particle trajectories. Inform Sciences 176:937–971. https://doi.org/10.1016/j.ins.2005.02.003

    MathSciNet  MATH  Google Scholar 

Download references

Funding

This research was funded by Fundamental Research Funds of Central Universities (2572018BF02), National Natural Science Foundation of China (31370710), Forestry Science and Technology Extension Project (2016[34]), the 948 Project from the Ministry of Forestry of China (2014-4-46) and the Postdoctoral Research Fund of Heilongjiang Province (LBH-Q13007)

Author information

Author notes

    Authors and Affiliations

    1. Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Luo Yu Road, Wuhan, 430074, China

      Bowen Wu

    2. Northeast Forestry University, School of Mechanical and Electrical Engineering, He Xing Road, Harbin, 15004, China

      Bowen Wu, Liangkuan Zhu & Xin Li

    Authors
    1. Bowen Wu
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Liangkuan Zhu
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Xin Li
      View author publications

      You can also search for this author in PubMed Google Scholar

    Contributions

    W.B. and Z.L. contributed to the idea of this paper; W.B. performed the experiments; All authors analyzed data; W.B. wrote the manuscript; W.B. and Z.L. contributed to the revision of this paper.

    Corresponding author

    Correspondence to Liangkuan Zhu.

    Ethics declarations

    Ethics

    standard This article does not contain any studies with human participants or animals performed by any of the authors.

    Conflict of Interests

    The authors declare no conflict of interest.

    Additional information

    Publisher’s note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Liangkuan Zhu and Xin Li contributed equally to this work.

    Rights and permissions

    Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Wu, B., Zhu, L. & Li, X. Giza pyramids construction algorithm with gradient contour approach for multilevel thresholding color image segmentation. Appl Intell 53, 21248–21267 (2023). https://doi.org/10.1007/s10489-023-04512-9

    Download citation

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s10489-023-04512-9

    Keywords

    Search

    Navigation