Skip to main content

Advertisement

SpringerLink
Log in

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

In image processing, multi-level thresholding is a sophisticated technique used to delineate regions of interest in images by identifying intensity levels that differentiate different structures or objects. Multi-range intensity partitioning captures the complexity and variability of an image. The aim of metaheuristic algorithms is to find threshold values that maximize intra-class differences and minimize inter-class differences. Various approaches and algorithms are reviewed and their advantages, limitations, and challenges are discussed in this paper. In addition, the review identifies future research areas such as handling complex images and inhomogeneous data, determining thresholding levels automatically, and addressing algorithm interpretation. The comprehensive review provides insights for future advancements in multilevel thresholding techniques that can be used by researchers in the field of image processing.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

The datasets generated and/or analyzed during the current study are not publicly available due but are available from the corresponding author on reasonable request.

References

  1. Qin J, Wang CT, Qin G (2019) A multilevel image thresholding method based on subspace elimination optimization, Hindawi. Math Probl Eng. https://doi.org/10.1155/2019/6706590

    Article  Google Scholar 

  2. Rafique AA, Gochoo M, Jalal A et al (2023) Maximum entropy scaled super pixels segmentation for multi-object detection and scene recognition via deep belief network. Multimed Tools Appl 82:13401–13430. https://doi.org/10.1007/s11042-022-13717-y

    Article  Google Scholar 

  3. Khorram B, Yazdi M (2019) A New optimized thresholding method using ant colony algorithm for MR brain image segmentation. J Digit Imaging 32:162–174. https://doi.org/10.1007/s10278-018-0111-x

    Article  Google Scholar 

  4. Dang T-V, Bui N-T (2023) Multi-scale fully convolutional network-based semantic segmentation for mobile robot navigation. Electronics 12:533. https://doi.org/10.3390/electronics12030533

    Article  Google Scholar 

  5. Yu J, Zhang J, Shu A, Chen Y, Chen J, Yang Y, Tang W, Zhang Y (2023) Study of convolutional neural network-based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction. Comput Electron Agric 209:107811. https://doi.org/10.1016/j.compag.2023.107811

    Article  Google Scholar 

  6. Schein KE, Herz M, Rauschnabel PA (2023) How do tourists evaluate augmented reality services? Segmentation, awareness, devices and marketing use cases, Nee AYC, Ong SK (eds) Springer Handbook of Augmented Reality. Springer, Cham, pp 451–469. https://doi.org/10.1007/978-3-030-67822-7_19

  7. Klingenberg S, Fischer R, Zettler I, Makransky G (2023) Facilitating learning in immersive virtual reality: Segmentation, summarizing, both or none? J Comput Assist Learn 39:218–230. https://doi.org/10.1111/jcal.12741

    Article  Google Scholar 

  8. Myagmar-Ochir Y, Kim W (2023) A survey of video surveillance systems in smart city. Electronics 12:3567. https://doi.org/10.3390/electronics12173567

    Article  Google Scholar 

  9. Luo Z, Yang W, Yuan Y, Gou R, Li X (2023) Semantic segmentation of agricultural images: a survey. Inform Process Agrice. https://doi.org/10.1016/j.inpa.2023.02.001

    Article  Google Scholar 

  10. Khairnar S, Thepade SD, Gite S (2021) Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU. Niblack, Burnsen, Thepade’s SBTC, Intell Syst Appli 10–11:200046. https://doi.org/10.1016/j.iswa.2021.200046

    Article  Google Scholar 

  11. Liu X, Song L, Liu S, Zhang Y (2021) A review of deep-learning-based medical image segmentation methods. Sustain J 13:1224. https://doi.org/10.3390/su13031224

    Article  Google Scholar 

  12. Ghosh S, Das N, Das I, Maulik U (2019) Understanding deep learning techniques for image segmentation. ACM Comput Surv 52:1–35

    Article  Google Scholar 

  13. Manoharan S (2020) Performance analysis of clustering based image segmentation techniques. J. Innov. Image Process. 2:14–24. https://doi.org/10.36548/jiip.2020.1.002

    Article  Google Scholar 

  14. Wenming C, Qifan L, He Z (2020) Review of pavement defect detection methods. IEEE Access 8:14531–14544. https://doi.org/10.1109/ACCESS.2020.2966881

    Article  Google Scholar 

  15. Houssein EH, El-din Helmy B, Oliva D, Elngar AA, Shaban H (2021) Multi-level Thresholding image segmentation based on nature-inspired optimization algorithms: a comprehensive review. In: Oliva D, Houssein EH, Hinojosa S (eds) Metaheuristics in machine learning: theory and applications. Studies in Computational Intelligence, Springer, Cham, 967:239–265. https://doi.org/10.1007/978-3-030-70542-8_11

  16. Elaziz MA, Nabil N, Moghdani R, Ewees AA, Cuevas E, Lu S (2021) Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm. Multimed Tools Appl 80:12435–12468. https://doi.org/10.1007/s11042-020-10313-w

    Article  Google Scholar 

  17. Salehnia T, Liu K, Xue Y, Tang W, Salehnia T (2022) A Multi-Level thresholding image segmentation method using hybrid arithmetic optimization and harris hawks optimizer algorithms. https://doi.org/10.2139/ssrn.4188471

  18. Sowjanya K, Kumar Injeti S (2021) Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding. Expert Syst Appls 182:115286. https://doi.org/10.1016/j.eswa.2021.115286

    Article  Google Scholar 

  19. Bhandari AK, Rahul K (2019) A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization. Appl Soft Comput 81:105515. https://doi.org/10.1016/j.asoc.2019.105515

    Article  Google Scholar 

  20. Mostafa RR, El-Attar NE, Sabbeh SF, Vidyarthi A, Hashim FA (2023) ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets. Soft Comput 27:13553–13581. https://doi.org/10.1007/s00500-022-07115-7

    Article  Google Scholar 

  21. Kamsyakawuni A, Sari MP, Riski A, Santoso KA (2020) Metaheuristic algorithm approach to solve non-linear equations system with complex roots. J Phys: Conf Ser 1494:23–24. https://doi.org/10.1088/1742-6596/1494/1/012001

    Article  Google Scholar 

  22. Jiang Y, Zhang D, Zhu W, Wang L (2023) Multi-level thresholding image segmentation based on improved slime mould algorithm and symmetric cross-entropy. Entropy 25:178. https://doi.org/10.3390/e25010178

    Article  Google Scholar 

  23. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95 - International Conference on Neural Networks, 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  24. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  25. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  26. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99. https://doi.org/10.1023/A:1022602019183

    Article  Google Scholar 

  27. Joshi AS, Kulkarni O, Kakandikar GM, Nandedkar VM (2017) Cuckoo search optimization- a review. Mater Today: Proc 4:7262–7269. https://doi.org/10.1016/j.matpr.2017.07.055

    Article  Google Scholar 

  28. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  29. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  30. Yang X-S, He X (2013) Firefly algorithm: recent advances and applications. Int Jf Swarm Intell 1:36–50. https://doi.org/10.1504/IJSI.2013.055801

    Article  Google Scholar 

  31. Mirjalili S (2016) SCA: a Sine Cosine Algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  32. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  33. Mahajan S, Pandit A (2022) Image segmentation and optimization techniques a short overview. Medicon Eng Themes 2:47–49

    Google Scholar 

  34. Hao S, Huang C, Heidari AA, Xu Z, Chen H, Alabdulkreem E, Elmannai H, Wang X (2023) Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images. Biomed Signal Process Control 86:105147. https://doi.org/10.1016/j.bspc.2023.105147

    Article  Google Scholar 

  35. Song S, Jia H, Ma J (2019) A chaotic electromagnetic field optimization algorithm based on fuzzy entropy for multilevel thresholding color image segmentation. Entropy 21:398. https://doi.org/10.3390/e21040398

    Article  MathSciNet  Google Scholar 

  36. Abdel-Basset M, Chang V, Mohamed R (2021) A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems. Neural Comput Appl 33:10685–10718. https://doi.org/10.1007/s00521-020-04820-y

    Article  Google Scholar 

  37. Wang S, Jia H, Peng X (2019) Modified salp swarm algorithm based multilevel thresholding for color image segmentation. Math Biosci Eng 17:700–724. https://doi.org/10.3934/mbe.2020036

    Article  MathSciNet  Google Scholar 

  38. Panda R, Samantaray L, Das A, Agrawal S, Abraham A (2021) A novel evolutionary row class entropy based optimal multi-level thresholding technique for brain MR images. Expert Syst Appl 168:114426. https://doi.org/10.1016/j.eswa.2020.114426

    Article  Google Scholar 

  39. Wunnava A, Naik MK, Panda R, Jena B, Abraham A (2022) A differential evolutionary adaptive Harris hawks optimization for two dimensional practical Masi entropy-based multilevel image thresholding. J King Saud Univ-Comput Inform Sci 34:3011–3024. https://doi.org/10.1016/j.jksuci.2020.05.001

    Article  Google Scholar 

  40. Pai AG, Buddhiraju KM, Durbha SS (2022) Quantum inspired genetic algorithm for bi-level thresholding of gray-scale images. The Int Archiv Photogramm Remote Sens Spatial Inform Sci XLVIII-4/W6:483–488

    Google Scholar 

  41. Naji Alwerfali HS, Al-qaness MAA, Elaziz MA, Ewees AA, Oliva D, Lu S (2020) Multi-level image thresholding based on modified spherical search optimizer and fuzzy entropy. Entropy. https://doi.org/10.3390/e22030328

    Article  MathSciNet  Google Scholar 

  42. Kumar A, Tiwari A (2019) A comparative study of otsu thresholding and K-means algorithm of image segmentation. Int J Eng Tech Res (IJETR) 9:2454–4698. https://doi.org/10.31873/IJETR.9.5.2019.62

    Article  Google Scholar 

  43. Li L, Sun L, Xue Y, Li S, Huang X, Mansour RF (2021) Fuzzy multilevel image thresholding based on improved coyote optimization algorithm. IEEE Access 9:33595–33607. https://doi.org/10.1109/ACCESS.2021.3060749

    Article  Google Scholar 

  44. Abdel-Basset M, Mohamed R, Abouhawwash M, Chakrabortty RK, Ryan MJ, Nam Y (2021) An improved jellyfish algorithm for multilevel thresholding of magnetic resonance brain image segmentations. Comput Mater Contin 68:2961–2977. https://doi.org/10.32604/cmc.2021.016956

    Article  Google Scholar 

  45. Qiao L, Liu K, Xue Y, Tang W, Salehnia T (2023) A multi-level thresholding image segmentation method using hybrid arithmetic optimization and harris hawks optimizer algorithms. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2023.122316

    Article  Google Scholar 

  46. Sathya PD, Kalyani R, Sakthivel VP (2021) Color image segmentation using Kapur, Otsu and minimum cross entropy functions based on exchange market algorithm. Expert Syst Appl 172:114636. https://doi.org/10.1016/j.eswa.2021.114636

    Article  Google Scholar 

  47. Hosny KM, Khalid AM, Hamza HM et al (2023) Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function. Neural Comput Appl 35:855–886. https://doi.org/10.1007/s00521-022-07718-z

    Article  Google Scholar 

  48. Unajan MC, Gerardo BD, Medina RP (2019) A modified otsu-based image segmentation algorithm (OBISA). In: Proceedings of the International Multi Conference of Engineers and Computer Scientists. pp 13–15

  49. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vision Graph Image Process 29:273–285. https://doi.org/10.1016/0734-189X(85)90125-2

    Article  Google Scholar 

  50. Abd BA, Alrawi ATH, Bassel A (2023) Optimization methods for image thresholding: a review. J Univ Anbar Pure Sci 17:137–148. https://doi.org/10.37652/juaps.2023.178875

    Article  Google Scholar 

  51. Sathya PD (2017) Tsallis entropy based multilevel image thresholding using chaotic particle swarm optimization algorithm. Int J Emerg Technol Comput Sci Electr (IJETCSE) 24

  52. Dhal KG, Ray S, Das A, Gálvez J, Das S (2019) Fuzzy multi-level color satellite image segmentation using nature-inspired optimizers: a comparative study. J Indian Soc Remote Sens 47:1391–1415. https://doi.org/10.1007/s12524-019-01005-6

    Article  Google Scholar 

  53. Xu L, Jia H, Lang C, Peng X, Sun K (2019) A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution. IEEE Access 7:19502–19538. https://doi.org/10.1109/ACCESS.2019.2896673

    Article  Google Scholar 

  54. Liu W, Huang Y, Ye Z, Cai W, Yang S, Cheng X, Frank I (2020) Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Appl Sci 10:3225. https://doi.org/10.3390/app10093225

    Article  Google Scholar 

  55. Nakane T, Bold N, Sun H, Lu X, Akashi T, Zhang C (2020) Application of evolutionary and swarm optimization in computer vision: a literature survey. IPSJ Trans Comput Vision Appl 12:3. https://doi.org/10.1186/s41074-020-00065-9

    Article  Google Scholar 

  56. Zhang J, Li C, Rahaman MM, Yao Y, Ma P, Zhang J, Zhao X, Jiang T, Grzegorzek M (2022) A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artif Intell Rev 55:2875–2944. https://doi.org/10.1007/s10462-021-10082-4

    Article  Google Scholar 

  57. Agrawal T, Choudhary P (2023) Segmentation and classification on chest radiography: a systematic survey. Vis Comput 39:875–913. https://doi.org/10.1007/s00371-021-02352-7

    Article  Google Scholar 

  58. Mittal H, Pandey AC, Saraswat M, Kumar S, Pal R, Modwel G (2022) A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimed Tools Appl 81:35001–35026. https://doi.org/10.1007/s11042-021-10594-9

    Article  Google Scholar 

  59. Punn NS, Agarwal S (2022) Modality specific U-Net variants for biomedical image segmentation: a survey. Artif Intell Rev 55:5845–5889. https://doi.org/10.1007/s10462-022-10152-1

    Article  Google Scholar 

  60. Loyola-González O, Medina-Pérez MA, Choo K-KR (2020) A review of supervised classification based on contrast patterns: applications, trends, and challenges. J Grid Comput 18:797–845. https://doi.org/10.1007/s10723-020-09526-y

    Article  Google Scholar 

  61. Iqbal A, Sharif M, Yasmin M, Raza M, Aftab S (2022) Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey, International Journal of Multimedia. Inf Retrieval 11:333–368. https://doi.org/10.1007/s13735-022-00240-x

    Article  Google Scholar 

  62. Ramadan H, Lachqar C, Tairi H (2020) A survey of recent interactive image segmentation methods. Comput Visual Media 6:355–384. https://doi.org/10.1007/s41095-020-0177-5

    Article  Google Scholar 

  63. Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vision 128:261–318. https://doi.org/10.1007/s11263-019-01247-4

    Article  Google Scholar 

  64. Rai R, Das A, Dhal KG (2022) Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. Evol Syst 13:889–945. https://doi.org/10.1007/s12530-022-09425-5

    Article  Google Scholar 

  65. Borji A, Cheng M-M, Hou Q, Jiang H, Li J (2019) Salient object detection: a survey. Comput Visual Media 5:117–150. https://doi.org/10.1007/s41095-019-0149-9

    Article  Google Scholar 

  66. Aljuaid A, Anwar M (2022) Survey of supervised learning for medical image processing. SN Comput Sci 3:292. https://doi.org/10.1007/s42979-022-01166-1

    Article  Google Scholar 

  67. Sasmal B, Dhal KG (2023) A survey on the utilization of Superpixel image for clustering based image segmentation. Multimed Tools Appl 82:35493–35555. https://doi.org/10.1007/s11042-023-14861-9

    Article  Google Scholar 

  68. Abualigah L, Almotairi KH, Elaziz MA (2023) Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: comparative analysis, open challenges and new trends. Appl Intell 53:11654–11704. https://doi.org/10.1007/s10489-022-04064-4

    Article  Google Scholar 

  69. Bagwari N, Kumar S, Verma VS (2023) A comprehensive review on segmentation techniques for satellite images. Archiv Comput Methods Eng 30:4325–4358. https://doi.org/10.1007/s11831-023-09939-4

    Article  Google Scholar 

  70. Morales-Castañeda B, Zaldívar D, Cuevas E, Fausto F, Rodríguez A (2020) A better balance in metaheuristic algorithms: Does it exist? Swarm Evol Comput 54:100671. https://doi.org/10.1016/j.swevo.2020.100671

    Article  Google Scholar 

  71. Agrawal P, Abutarboush HF, Ganesh T, Mohamed AW (2021) Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019). IEEE Access 9:26766–26791. https://doi.org/10.1109/ACCESS.2021.3056407

    Article  Google Scholar 

  72. Amiriebrahimabadi M, Mansouri N (2023) A comprehensive survey of feature selection techniques based on whale optimization algorithm. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-17329-y

    Article  Google Scholar 

  73. Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W (2022) Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082. https://doi.org/10.1016/j.engappai.2022.105082

    Article  Google Scholar 

  74. Talatahari S, Azizi M, Gandomi AH (2021) Material generation algorithm: a novel metaheuristic algorithm for optimization of engineering problems. Processes 9:859. https://doi.org/10.3390/pr9050859

    Article  Google Scholar 

  75. Jardim S, António J, Mora C (2023) Image thresholding approaches for medical image segmentation - short literature review. Proc Comput Sci 219:1485–1492. https://doi.org/10.1016/j.procs.2023.01.439

    Article  Google Scholar 

  76. Manic KS, Al Naimi IS, Hasoon FN, Rajinikanth V (2023) Jaya algorithm-assisted evaluation of tooth elements using digital bitewing radiography images, research anthology on improving medical imaging techniques for analysis and intervention, edited by Information Resources Management Association, IGI Global, pp 606–628. https://doi.org/10.4018/978-1-6684-7544-7.ch030

  77. Dorathi Jayaseeli JD, Malathi D (2020) An efficient automated road region extraction from high resolution satellite images using improved cuckoo search with multi-level thresholding schema. Proc Comput Sci 167:1161–1170. https://doi.org/10.1016/j.procs.2020.03.418

    Article  Google Scholar 

  78. Hinojosa S, Avalos O, Gálvez J, Oliva D, Cuevas E, Pérez-Cisneros M (2018) Remote sensing imagery segmentation based on multi-objective optimization algorithms. In: IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp 1–6. https://doi.org/10.1109/LA-CCI.2018.8625215

  79. Abd BA, Alrawi ATH, Bassel A (2023) A multilevel image thresholding based on hybrid Jaya algorithm and simulated annealing, 17:149–157. https://doi.org/10.37652/juaps.2023.178876

  80. David B, Gomathi R (2023) Improved segmentation with optimization based multilevel thresholding and K-means clustering for plant disease identification, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-2373358/v1

  81. Shuai W, Yusof Y (2023) Insulator fault diagnosis based on multi-objectives multilevel thresholding method and boost particle swarm optimization. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01243-5

    Article  Google Scholar 

  82. Bai Y, Zhang B, Xu N, Zhou J, Shi J, Diao Z (2023) Vision-based navigation and guidance for agricultural autonomous vehicles and robots: a review. Comput Electron Agric 205:107584. https://doi.org/10.1016/j.compag.2022.107584

    Article  Google Scholar 

  83. Akinbade D, Ogunde AO, Odim MO, Oguntunde BO (2020) An adaptive thresholding algorithm-based optical character recognition system for information extraction in complex images. J Comput Sci 16:784–801. https://doi.org/10.3844/jcssp.2020.784.801

    Article  Google Scholar 

  84. Ivanov I, Karparov V, Kutryanska M, Bosakova-Ardenska A, Panayotov P (2021) Application of image processing with multilevel thresholding for mould detection on blue cheese cut surface

  85. Chaabane SB, Harrabi R, Bushnag A, Seddik H (2022) Iris recognition based on multilevel thresholding technique and modified fuzzy c-means algorithm. J Artif Intell 4:201–214. https://doi.org/10.32604/jai.2022.032850

    Article  Google Scholar 

  86. Li Y, Bai X, Jiao L, Xue Y (2017) Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl Soft Comput 56:345–356. https://doi.org/10.1016/j.asoc.2017.03.018

    Article  Google Scholar 

  87. He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240:152–174. https://doi.org/10.1016/j.neucom.2017.02.040

    Article  Google Scholar 

  88. Suresh S, Lal S (2017) Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images. Appl Soft Comput 55:503–522. https://doi.org/10.1016/j.asoc.2017.02.005

    Article  Google Scholar 

  89. Ishak AB (2017) An improved marine predators algorithm with fuzzy entropy for multi-level thresholding: real world example of COVID-19 CT image segmentation image segmentation. Appl Soft Comput 52:306–322. https://doi.org/10.1016/j.asoc.2016.10.034

    Article  MathSciNet  Google Scholar 

  90. Aziz MA, 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

    Article  Google Scholar 

  91. Md Khairuzzaman AK, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76. https://doi.org/10.1016/j.eswa.2017.04.029

    Article  Google Scholar 

  92. Jac Fredo AR, Abilash RS, Suresh Kumar C (2017) Segmentation and analysis of damages in composite images using multi-level threshold methods and geometrical features. Measurement 100:270–278. https://doi.org/10.1016/j.measurement.2017.01.002

    Article  Google Scholar 

  93. Sridevi M (2017) Image segmentation based on multilevel thresholding using firefly algorithm. In: International Conference on Inventive Computing and Informatics (ICICI), pp 750–753. https://doi.org/10.1109/ICICI.2017.8365235

  94. Chen H, Deng X, Yan L, Ye Z (2017) Multilevel thresholding selection based on the fireworks algorithm for image segmentation. In: International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp175–180. https://doi.org/10.1109/SPAC.2017.8304271

  95. Wei H, Yang Q (2017) A multilevel threshold segmentation technique using self-adaptive Cuckoo search algorithm. Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp 2292–2295. https://doi.org/10.1109/IAEAC.2017.8054429

  96. Maryam H, Mustapha A, Younes J (2017) A multilevel thresholding method for image segmentation based on multi objective particle swarm optimization, International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp 1–6. https://doi.org/10.1109/WITS.2017.7934620

  97. Mittal H, Saraswat M (2018) An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm. Eng Appl Artif Intell 71:226–235. https://doi.org/10.1016/j.engappai.2018.03.001

    Article  Google Scholar 

  98. Li J, Tang W, Wang J, Zhang X (2018) Multilevel thresholding selection based on variational mode decomposition for image segmentation. Signal Process 147:80–91. https://doi.org/10.1016/j.sigpro.2018.01.022

    Article  Google Scholar 

  99. Hao G, Zheng F, Chi-Man P, Haidong H, Rushi L (2018) A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm. Comput Electr Eng 70:931–938. https://doi.org/10.1016/j.compeleceng.2017.12.037

    Article  Google Scholar 

  100. Choi J, Choi HH-S, Chen M (2018) Multi-level thresholding grayscale image segmentation implemented with genetic algorithm. In: IEEE MIT Undergraduate Research Technology Conference (URTC), pp 1–5. https://doi.org/10.1109/URTC45901.2018.9244772

  101. Sambandam RK, Jayaraman S (2018) Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images. J King Saud Univ-Comput Inform Sci 30:449–461. https://doi.org/10.1016/j.jksuci.2016.11.002

    Article  Google Scholar 

  102. Kotte S, Pullakura RK, Injeti SK (2018) Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization. Measurement 130:340–361. https://doi.org/10.1016/j.measurement.2018.08.007

    Article  Google Scholar 

  103. Rapaka S, Kumar Pullakura R (2018) Towards segmentation of non-ideal iris images using optimization based multilevel thresholding, International Conference on Communication and Electronics Systems (ICCES), pp 46–51. https://doi.org/10.1109/CESYS.2018.8723939

  104. Mahdi FP, Kobashi S (2018) Quantum particle swarm optimization for multilevel thresholding-based image segmentation on dental X-Ray images. In: International Conference on Soft Computing and Intelligent Systems (SCIS) and International Symposium on Advanced Intelligent Systems (ISIS), pp 1148–1153. https://doi.org/10.1109/SCIS-ISIS.2018.00181

  105. Ventura de Oliveira P, Yamanaka K (2018) Image segmentation using multilevel thresholding and genetic algorithm: an approach. In: International Conference on Data Science and Business Analytics (ICDSBA), pp 380–385. https://doi.org/10.1109/ICDSBA.2018.00078

  106. Kumar AS, Kumar A, Bajaj V, Singh GK (2018) Fractional-order darwinian swarm intelligence inspired multilevel thresholding for mammogram segmentation. In: International Conference on Communication and Signal Processing (ICCSP), pp 0160–0164. https://doi.org/10.1109/ICCSP.2018.8524302

  107. Wang Y, Zhang G (2019) Multi-level thresholding selection based on multi-verse optimization with levy flight for image segmentation. In: IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp 1058–1063. https://doi.org/10.1109/ISKE47853.2019.9170413

  108. Elaziz MA, Bhattacharyya S, Lu S (2019) Swarm selection method for multilevel thresholding image segmentation. Expert Syst Appl 138:112818. https://doi.org/10.1016/j.eswa.2019.07.035

    Article  Google Scholar 

  109. Elaziz MA, Lu S (2019) Many-objectives multilevel thresholding image segmentation using Knee Evolutionary Algorithm. Expert Syst Appl 125:305–316. https://doi.org/10.1016/j.eswa.2019.01.075

    Article  Google Scholar 

  110. Tarkhaneh O, Shen H (2019) An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation. Expert Syst Appl 138:112820. https://doi.org/10.1016/j.eswa.2019.07.037

    Article  Google Scholar 

  111. Elaziz MA, Oliva D, Ewees AA, Xiong S (2019) Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer. Expert Syst Appl 125:112–129. https://doi.org/10.1016/j.eswa.2019.01.047

    Article  Google Scholar 

  112. Rodríguez-Esparza E, Zanella-Calzada LA, Oliva D, Hinojosa S, Pérez-Cisneros M (2019) Multilevel segmentation for automatic detection of malignant masses in digital mammograms based on threshold comparison. In: IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp 1–6. https://doi.org/10.1109/LA-CCI47412.2019.9037030

  113. Iyer S, Nadkarni AP, Padmini TN (2019) Antlion optimization and Whale optimization Algorithm for multilevel thresholding segmentation, Innovations in Power and Advanced Computing Technologies (i-PACT), 1:1–8. https://doi.org/10.1109/i-PACT44901.2019.8960178

  114. Ahilan A, Manogaran G, Raja C, Kadry S, Kumar SN, Agees Kumar S, Jarin T, Krishnamoorthy S, Kumar PM, Babu GC, Senthil Murugan N, Parthasarathy G (2019) Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images. IEEE Access 7:89570–89580. https://doi.org/10.1109/ACCESS.2019.2891632

    Article  Google Scholar 

  115. Bao X, Jia H, Lang C (2019) A novel hybrid harris hawks optimization for color image multilevel thresholding segmentation. IEEE Access 7:76529–76546. https://doi.org/10.1109/ACCESS.2019.2921545

    Article  Google Scholar 

  116. Jia H, Ma J, Song W (2019) Multilevel thresholding segmentation for color image using modified moth-flame optimization. IEEE Access 7:44097–44134. https://doi.org/10.1109/ACCESS.2019.2908718

    Article  Google Scholar 

  117. Liang H, Jia H, Xing Z, Ma J, Peng X (2019) Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7:11258–11295. https://doi.org/10.1109/ACCESS.2019.2891673

    Article  Google Scholar 

  118. Kalyani R, Sathya PD, Sakthivel VP (2020) Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy. Eng Sci Technol Int J 23:1327–1341. https://doi.org/10.1016/j.jestch.2020.07.007

    Article  Google Scholar 

  119. Xing Z (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

    Article  Google Scholar 

  120. He L, Huang S (2020) An efficient krill herd algorithm for color image multilevel thresholding segmentation problem. Appl Soft Comput 89:106063. https://doi.org/10.1016/j.asoc.2020.106063

    Article  Google Scholar 

  121. Mousavirad SJ, Ebrahimpour-Komleh H (2020) Human mental search-based multilevel thresholding for image segmentation. Appl Soft Comput 97:105427. https://doi.org/10.1016/j.asoc.2019.04.002

    Article  Google Scholar 

  122. Elaziz MA, Ewees AA, Yousri D, Naji Alwerfali HS, Awad QA, Lu S, Al-Qness MAA (2020) An improved marine predators algorithm with fuzzy entropy for multi-level thresholding: real world example of COVID-19 CT image segmentation. IEEE Access 8:125306–125330. https://doi.org/10.1109/ACCESS.2020.3007928

    Article  Google Scholar 

  123. Ewees AA, Abd Elaziz M, Al-Qaness MAA, Khalil HA, Kim S (2020) Improved artificial bee colony using sine-cosine algorithm for multi-level thresholding image segmentation. IEEE Access 8:26304–26315. https://doi.org/10.1109/ACCESS.2020.2971249

    Article  Google Scholar 

  124. Zhang Z, Yin J (2020) Bee foraging algorithm based multi-level thresholding for image segmentation. IEEE Access 8:16269–16280. https://doi.org/10.1109/ACCESS.2020.2966665

    Article  Google Scholar 

  125. Ahammad SH, Ur Rahman MZ, Lay-Ekuakille A, Giannoccaro NI (2020) An Efficient optimal threshold-based segmentation and classification model for multi-level spinal cord Injury detection. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp 1–6. https://doi.org/10.1109/MeMeA49120.2020.9137122

  126. Wu B, Zhou J, Ji X, Yin Y, Shen X (2020) An ameliorated teaching–learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur’s entropy and Otsu’s between class variance. Inf Sci 533:72–107. https://doi.org/10.1016/j.ins.2020.05.033

    Article  MathSciNet  Google Scholar 

  127. Elaziz MA, Ewees AA, Oliva D (2020) Hyper-heuristic method for multilevel thresholding image segmentation. Expert Syst Appl 146:113201. https://doi.org/10.1016/j.eswa.2020.113201

    Article  Google Scholar 

  128. Yan Z, Zhang J, Tang J (2020) Sine cosine algorithm for underwater multilevel thresholding image segmentation. Global Oceans. https://doi.org/10.1109/IEEECONF38699.2020.9389009

    Article  Google Scholar 

  129. Devanathan B, Venkatachalapathy K (2020) An optimal multilevel thresholding based segmentation and classification model for brain tumor diagnosis. In: International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp 1133–1138. https://doi.org/10.1109/ICECA49313.2020.9297571

  130. Malakar P, Ghosh D, Shaw K, Pandey P, Das S, Dhabal S (2020) Multilevel Thresholding based Image Segmentation using Optimization Algorithm. In: IEEE International Conference for Convergence in Engineering (ICCE), pp 335–339. https://doi.org/10.1109/ICCE50343.2020.9290582

  131. Li H, Zheng G, Sun K, Jiang Z, Li Y, Jia H (2020) A logistic chaotic barnacles mating optimizer with Masi entropy for color image multilevel thresholding segmentation. IEEE Access 8:213130–213153. https://doi.org/10.1109/ACCESS.2020.3040177

    Article  Google Scholar 

  132. Yousef HA, Moussa EMM, Abdel-Razek MZM, El-Kholy MMSA, Hasan LHS, El-Sayed AE-DAM, Salek MAK, Omar MKM (2021) Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation. Egypt J Radiol Nucl Med 52:293. https://doi.org/10.1186/s43055-021-00602-1

    Article  Google Scholar 

  133. 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 113:107905. https://doi.org/10.1016/j.asoc.2021.107905

    Article  Google Scholar 

  134. Houssein EH, Hussain K, Abualigah L, Elaziz MA, Alomoush W, Dhiman G, Djenouri Y, Cuevas E (2021) An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation. Knowl-Based Syst 229:107348. https://doi.org/10.1016/j.knosys.2021.107348

    Article  Google Scholar 

  135. Patra DK, Si T, Mondal S, Mukherjee P (2021) Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization. Biomed Signal Process Control 69:102925. https://doi.org/10.1016/j.bspc.2021.102925

    Article  Google Scholar 

  136. Zhao S, Wang P, Heidari AA, Chen H, Turabieh H, Mafarja M, Li C (2021) Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Comput Biol Med 134:104427. https://doi.org/10.1016/j.compbiomed.2021.104427

    Article  Google Scholar 

  137. Resma KPB, Nair MS (2021) Multilevel thresholding for image segmentation using Krill Herd Optimization algorithm. J King Saud Univ Comput Inform Sci 33:528–541. https://doi.org/10.1016/j.jksuci.2018.04.007

    Article  Google Scholar 

  138. Yang G, Liu Z, Zhu Z (2021) Multi-level threshold segmentation based on LSHADE. In: International Conference on Digital Society and Intelligent Systems (DSInS), pp 204–211. https://doi.org/10.1109/DSInS54396.2021.9670556

  139. Houssein EH, Emam MM, Ali AA (2021) An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm. Expert Syst Appl 185:115651. https://doi.org/10.1016/j.eswa.2021.115651

    Article  Google Scholar 

  140. Srikanth R, Bikshalu K (2021) Multilevel thresholding image segmentation based on energy curve with harmony Search Algorithm. Ain Shams Eng J 12:1–20. https://doi.org/10.1016/j.asej.2020.09.003

    Article  Google Scholar 

  141. Houssein EH, Helmy BE, Oliva D, Elngar AA, Shaban H (2021) A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation. Expert Syst Appl 167:114159. https://doi.org/10.1016/j.eswa.2020.114159

    Article  Google Scholar 

  142. Rahaman J, Sing M (2021) An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm. Expert Syst Appl 174:114633. https://doi.org/10.1016/j.eswa.2021.114633

    Article  Google Scholar 

  143. Dinkar SK, Deep K, Mirjalili S, Thapliyal S (2021) Opposition-based Laplacian equilibrium optimizer with application in image segmentation using multilevel thresholding. Expert Syst Appl 174:114766. https://doi.org/10.1016/j.eswa.2021.114766

    Article  Google Scholar 

  144. Cheng X-w, Wang H-q, Chen G-C (2021) An improved whale optimization algorithm for dinosaur lantern festival color image multilevel thresholding segmentation. In: International Conference on Pattern Recognition and Artificial Intelligence (PRAI), pp 28–34. https://doi.org/10.1109/PRAI53619.2021.9551031

  145. Wang H-Q, Cheng X-W, Chen G-C (2021) A hybrid adaptive quantum behaved particle swarm optimization algorithm based multilevel thresholding for image segmentation. In: IEEE International Conference on Information Communication and Software Engineering (ICICSE), pp 97–102. https://doi.org/10.1109/ICICSE52190.2021.9404104

  146. Yan Z, Zhang J, Yang Z, Tang J (2021) Kapur’s entropy for underwater multilevel thresholding image segmentation based on whale optimization algorithm. IEEE Access 9:41294–41319. https://doi.org/10.1109/ACCESS.2020.3005452

    Article  Google Scholar 

  147. Chen X, Huang H, Heidari AA, Sun C, Lv Y, Gui W, Liang G, Gu Z, Chen H, Li C, Chen P (2022) An efficient multilevel thresholding image segmentation method based on the slime mould algorithm with bee foraging mechanism: a real case with lupus nephritis images. Comput Biol Med 142:105179. https://doi.org/10.1016/j.compbiomed.2021.105179

    Article  Google Scholar 

  148. Kumar A, Kumar A, Vishwakarma A, Kumar Singh G (2022) Multilevel thresholding for crop image segmentation based on recursive minimum cross entropy using a swarm-based technique. Comput Electron Agric 203:107488. https://doi.org/10.1016/j.compag.2022.107488

    Article  Google Scholar 

  149. Ren L, Zhao D, Zhao X, Chen W, Li L, Wu T, Liang G, Cai Z, Xu S (2022) Multi-level thresholding segmentation for pathological images: optimal performance design of a new modified differential evolution. Comput Biol Med 148:105910. https://doi.org/10.1016/j.compbiomed.2022.105910

    Article  Google Scholar 

  150. Ma G, Yue X (2022) An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Eng Appl Artif Intell 113:104960. https://doi.org/10.1016/j.engappai.2022.104960

    Article  Google Scholar 

  151. Su H, Zhao D, Elmannai H, Heidari AA, Bourouis S, Wu Z, Cai Z, Gui W, Chen M (2022) Multilevel threshold image segmentation for COVID-19 chest radiography: a framework using horizontal and vertical multiverse optimization. Comput Biol Med 146:105618. https://doi.org/10.1016/j.compbiomed.2022.105618

    Article  Google Scholar 

  152. Zhang Y, Xie H, Sun J, Zhang H (2022) An efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic system and Otsu threshold segmentation. Comput Biol Med 146:105542. https://doi.org/10.1016/j.compbiomed.2022.105542

    Article  Google Scholar 

  153. Chakraborty S, Mali K (2022) Biomedical image segmentation using fuzzy multilevel soft thresholding system coupled modified cuckoo search. Biomed Signal Process Control 72:103324. https://doi.org/10.1016/j.bspc.2021.103324

    Article  Google Scholar 

  154. Abdel-Basset M, Mohamed R, AbdelAziz NM, Abouhawwash M (2022) HWOA: a hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation. Expert Syst Appl 190:116145. https://doi.org/10.1016/j.eswa.2021.116145

    Article  Google Scholar 

  155. Fakri NFM, Zakaria NF, Sulaiman MH, Karim RA, Arshad NW, Wahab YA (2022) A multilevel thresholding algorithm for image segmentation based on barnacle mating optimization. In: Engineering Technology International Conference (ETIC), Online Conference, pp 504–511. https://doi.org/10.1049/icp.2022.2672

  156. Priya A, Agrawal RK, Rana B (2022) Fusion-based multilevel thresholding for image segmentation using evolutionary algorithm. In: IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pp 1–7. https://doi.org/10.1109/UPCON56432.2022.9986438

  157. Turajlic E, Buza E, Akagic A (2022) Honey Badger Algorithm and chef-based optimization algorithm for multilevel thresholding image segmentation, Telecommunications Forum (TELFOR), pp 1–4. https://doi.org/10.1109/TELFOR56187.2022.9983775

  158. Bhavani HR,. Champa HN (2022) A multilevel thresholding method based on HPSO for the segmentation of various objective functions. In: International Conference on Communication, Computing and Internet of Things (IC3IoT), pp 1–5. https://doi.org/10.1109/IC3IOT53935.2022.9767970

  159. Jayaprakash K, Balamurugan SP (2022) Design of optimal multilevel thresholding based segmentation with AlexNet model for plant leaf disease diagnosis. IN: International Conference on Smart Systems and Inventive Technology (ICSSIT), pp 1473–1479. https://doi.org/10.1109/ICSSIT53264.2022.9716233

  160. Singh S, Mittal N, Nayyar A, Singh U, Singh S (2023) A hybrid transient search naked mole-rat optimizer for image segmentation using multilevel thresholding. Expert Syst Appl 213:119021. https://doi.org/10.1016/j.eswa.2022.119021

    Article  Google Scholar 

  161. Wang J, Bei J, Song H, Zhang H, Zhang P (2023) A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation. Appl Soft Comput 137:110130. https://doi.org/10.1016/j.asoc.2023.110130

    Article  Google Scholar 

  162. Yang X, Wang R, Zhao D, Yu F, Heidari AA, Xu Z, Chen H, Algarni AD, Elmannai H, Xu S (2023) Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution. Biomed Signal Process Control 80:104373. https://doi.org/10.1016/j.bspc.2022.104373

    Article  Google Scholar 

  163. Gharehchopogh FS, Ibrikci T (2023) An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16300-1

    Article  Google Scholar 

  164. Liu Q, Qi Q, Li N (2023) Federated opposite learning based arithmetic optimization algorithm for image segmentation using multilevel thresholding. In: International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp 1257–1262. https://doi.org/10.1109/CSCWD57460.2023.10152600

  165. Søgaard J, Krasula LK, Shahid M, Temel D, Brunnström K, Razaak M (2016) Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming. Symposium on Electronic Imaging, 28. https://doi.org/10.2352/ISSN.2470-1173.2016.13.IQSP-206

  166. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  167. Jasak Z (2018) Benford’s Law and Wilcoxon test. J Math Sci Adv Appl 52:69–81. https://doi.org/10.18642/jmsaa_7100121981

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, 76135-133, Iran

    Mohammad Amiriebrahimabadi, Zhina Rouhi & Najme Mansouri

Authors
  1. Mohammad Amiriebrahimabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhina Rouhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Najme Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Najme Mansouri.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

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

Amiriebrahimabadi, M., Rouhi, Z. & Mansouri, N. A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10093-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11831-024-10093-8

Search

Navigation