Abstract
In a variety of image processing applications, multilevel thresholding image segmentation has gotten a lot of interest. When traditional approaches are utilised, however, the process of obtaining the ideal threshold values takes time. Despite the fact that Hybrid metaheuristic methods can be used to overcome these limits, such approaches may be ineffective when dealing with a local solution. The present study proposes a multi-level image thresholding based hybridization strategy based Sine-Cosine Crow Search Algorithm(SCCSA) to make more efficient image segmentation. The main limitation of the classical Crow Search Algorithm (CSA) is that search agents sometimes do not produce the best solutions. To update a solution to the best solution, each search agent can use Sine-Cosine Algorithm (SCA) movements to update its position accordingly. This ensures a good balance between two goals (exploration and exploitation) would improve the efficiency of the search algorithm. The optimal threshold values are searched by the chosen objective functions of the otsu’s and kapur’s entropy approaches. The hybrid algorithm is evaluated in 12 standard image sets and then compared with the performance of other state-of-the-art algorithms such as ICSA, SCA, CSA and ABC. Experimental results showed that, in different metrics of the output such as objective function values, PSNR, STD values, Mean, SSIM, FSIM and CPU time, the proposed algorithm is consistently higher than other algorithms. In addition, the wilcoxon test is performed using the proposed algorithm to detect the significant differences between the other algorithms. The findings indicated that the proposed SCCSA succeeds with other well-known algorithms and has dominance over robust, accurate and convergent values.
Similar content being viewed by others
References
Abualigah L, Al-Okbi NK, Elaziz MA, Houssein EH (2022) Boosting marine predators algorithm by Salp swarm algorithm for multilevel thresholding image segmentation. Multimed tools Appl 1–36. https://doi.org/10.1007/s11042-022-12001-3
Abualigah L, Ewees AA, Al-qaness MAA et al (2022) Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Comput Applic 34:8823–8852. https://doi.org/10.1007/s00521-022-06906-1
Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30. https://doi.org/10.1016/j.swevo.2013.02.001
Agrawal V, Rastogi R, Tiwari DC (2018) Spider monkey optimization: a survey. Int J Syst Assur Eng Manag 9:929–941. https://doi.org/10.1007/s13198-017-0685-6
Ahmadi M, Kazemi K, Aarabi A, Niknam T, Helfroush MS (2019) Image segmentation using multilevel thresholding based on modified bird mating optimization. Multimed Tools Appl 78:23003–23027. https://doi.org/10.1007/s11042-019-7515-6
Alwerfali HSN, Abd Elaziz M, Al-Qaness MAA et al (2019) A multilevel image thresholding based on hybrid salp swarm algorithm and fuzzy entropy. IEEE Access 7:181405–181422. https://doi.org/10.1109/access.2019.2959325
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
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
Baby Resma KP, Nair MS (2021) Multilevel thresholding for image segmentation using krill herd optimization algorithm. J King Saud Univ - Comput Inf Sci 33:528–541. https://doi.org/10.1016/j.jksuci.2018.04.007
Bhandari AK, Kumar A, Singh GK (2015) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 42:1573–1601. https://doi.org/10.1016/j.eswa.2014.09.049
Bhargava A, Bansal A (2018) Fruits and vegetables quality evaluation using computer vision: a review. J King Saud Univ - Comput Inf Sci https://doi.org/10.1016/j.jksuci.2018.06.002
Chakraborty F, Roy PK, Nandi D (2019) Oppositional elephant herding optimization with dynamic Cauchy mutation for multilevel image thresholding. Evol Intell 12:445–467. https://doi.org/10.1007/s12065-019-00238-1
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
Cuevas E, Sención F, Zaldivar D, Pérez-Cisneros M, Sossa H (2012) A multi-threshold segmentation approach based on artificial bee Colony optimization. Appl Intell 37:321–336. https://doi.org/10.1007/s10489-011-0330-z
Das S, Nayak GK, Saba L et al (2022) An artificial intelligence framework and its bias for brain tumor segmentation: a narrative review. Comput Biol Med 143:105273. https://doi.org/10.1016/j.compbiomed.2022.105273
Dehshibi MM, Sourizaei M, Fazlali M, Talaee O, Samadyar H, Shanbehzadeh J (2017) A hybrid bio-inspired learning algorithm for image segmentation using multilevel thresholding. Multimed Tools Appl 76:15951–15986. https://doi.org/10.1007/s11042-016-3891-3
Du E, Ives R, van Nevel A, She J-H (2011) advanced image processing for defense and security applications. EURASIP J Adv Signal Process 2010: https://doi.org/10.1155/2010/432972
Duan L, Yang S, Zhang D (2021) Multilevel thresholding using an improved cuckoo search algorithm for image segmentation. J Supercomput 77:6734–6753. https://doi.org/10.1007/s11227-020-03566-7
El Aziz MA, Ewees AA, Hassanien AE (2016) Hybrid swarms optimization based image segmentation. In: Hybrid soft computing for image segmentation. Springer International Publishing, Cham, pp 1–21
Ewees AA, Abd Elaziz M, Oliva D (2018) Image segmentation via multilevel thresholding using hybrid optimization algorithms. J Electron Imag 27:1. https://doi.org/10.1117/1.jei.27.6.063008
Fayaz S, Parah SA, Qureshi GJ (2022) Underwater object detection: architectures and algorithms – a comprehensive review. Multimed Tools Appl 81:20871–20916. https://doi.org/10.1007/s11042-022-12502-1
Gonzalez RC, Woods RE (2008) Digital image processing: international edition, 3rd edn. Pearson, Upper Saddle River, NJ
Grosan C, Abraham A (2007) Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Hybrid evolutionary algorithms. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 1–17
Gupta S, Deep K (2020) Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation. Neural Comput Applic 32:9521–9543. https://doi.org/10.1007/s00521-019-04465-6
Hammouche K, Diaf M, Siarry P (2010) A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Eng Appl Artif Intell 23:676–688. https://doi.org/10.1016/j.engappai.2009.09.011
Horng M-H (2010) Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst Appl 37:4580–4592. https://doi.org/10.1016/j.eswa.2009.12.050
Horng M-H, Liou R-J (2011) Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst Appl 38:14805–14811. https://doi.org/10.1016/j.eswa.2011.05.069
Jiang Z, Zou F, Chen D, Kang J (2021) An improved teaching–learning-based optimization for multilevel thresholding image segmentation. Arab J Sci Eng 46:8371–8396. https://doi.org/10.1007/s13369-021-05483-0
Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Proc 29:273–285. https://doi.org/10.1016/0734-189x(85)90125-2
Karakoyun M, Gülcü Ş, Kodaz H (2021) D-MOSG: discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding. Eng Sci Technol Int J 24:1455–1466. https://doi.org/10.1016/j.jestch.2021.03.011
Khairuzzaman AKM, 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
Khalilpourazari S, Pasandideh SHR (2020) Sine–cosine crow search algorithm: theory and applications. Neural Comput Applic 32:7725–7742. https://doi.org/10.1007/s00521-019-04530-0
Kotte S, Rajesh Kumar P, Injeti SK (2018) An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm. Ain Shams Eng J 9:1043–1067. https://doi.org/10.1016/j.asej.2016.06.007
Mahajan S, Mittal N, Pandit AK (2021) Image segmentation using multilevel thresholding based on type II fuzzy entropy and marine predators algorithm. Multimed Tools Appl 80:19335–19359. https://doi.org/10.1007/s11042-021-10641-5
Mlakar U, Potočnik B, Brest J (2016) A hybrid differential evolution for optimal multilevel image thresholding. Expert Syst Appl 65:221–232. https://doi.org/10.1016/j.eswa.2016.08.046
Mohan A, Poobal S (2018) Crack detection using image processing: a critical review and analysis. Alex Eng J 57:787–798. https://doi.org/10.1016/j.aej.2017.01.020
Mousavirad SJ, Ebrahimpour-Komleh H (2017) Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms. Evol Intell 10:45–75. https://doi.org/10.1007/s12065-017-0152-y
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
Naji Alwerfali HS, AA Al-Qaness M, Abd Elaziz M et al (2020) Multi-level image thresholding based on modified spherical search optimizer and fuzzy entropy. Entropy (Basel) 22:328. https://doi.org/10.3390/e22030328
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. https://doi.org/10.1109/tsmc.1979.4310076
Ouadfel S, Taleb-Ahmed A (2016) Performance study of harmony search algorithm for multilevel thresholding. J Intell Syst 25:473–513. https://doi.org/10.1515/jisys-2014-0147
Ouadfel S, Taleb-Ahmed A (2016) Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl 55:566–584. https://doi.org/10.1016/j.eswa.2016.02.024
Rahkar Farshi T (2019) A multilevel image thresholding using the animal migration optimization algorithm. Iran J Comput Sci 2:9–22. https://doi.org/10.1007/s42044-018-0022-5
Renugambal A, Selva Bhuvaneswari K (2021) Kapur’s entropy based hybridised WCMFO algorithm for brain MR image segmentation. IETE J res 1–20. https://doi.org/10.1080/03772063.2021.1906765
Saranya K, Selva Bhuvaneswari K (2022) Semantic annotation of land cover remote sensing images using fuzzy CNN. Intell autom soft comput 33:399–414. https://doi.org/10.32604/iasc.2022.023149
Sarkar S, Sen N, Kundu A, das S, Sinha Chaudhuri S (2013) A differential evolutionary multilevel segmentation of near infra-red images using Renyi’s entropy. In: Advances in intelligent systems and computing. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 699–706
Sathya PD, Kayalvizhi R (2010) PSO-based Tsallis thresholding selection procedure for image segmentation. Int J Comput Appl 5:39–46. https://doi.org/10.5120/903-1279
Sathya PD, Kayalvizhi R (2011) Optimal multilevel thresholding using bacterial foraging algorithm. Expert Syst Appl 38:15549–15564. https://doi.org/10.1016/j.eswa.2011.06.004
Shen L, Fan C, Huang X (2018) Multi-level image thresholding using modified flower pollination algorithm. IEEE Access 6:30508–30519. https://doi.org/10.1109/access.2018.2837062
Singh Gill H, Singh Khehra B, Singh A, Kaur L (2019) Teaching-learning-based optimization algorithm to minimize cross entropy for selecting multilevel threshold values. Egypt Inform J 20:11–25. https://doi.org/10.1016/j.eij.2018.03.006
Tuba E, Alihodzic A, Tuba M (2017) Multilevel image thresholding using elephant herding optimization algorithm. In: 2017 14th International Conference on Engineering of Modern Electric Systems (EMES). IEEE
Yamini B, Sabitha R (2022) Image steganalysis: real-time adaptive colour image segmentation for hidden message retrieval and Matthew’s correlation coefficient calculation. Int J Inf Comput Secur 17:83. https://doi.org/10.1504/ijics.2022.121292
Ye Z, Zheng Z, Yu X, Ning X (2006) Automatic threshold selection based on ant colony optimization algorithm. In: 2005 International conference on neural networks and brain. IEEE
Ye Z-W, Wang M-W, Liu W, Chen S-B (2015) Fuzzy entropy based optimal thresholding using bat algorithm. Appl Soft Comput 31:381–395. https://doi.org/10.1016/j.asoc.2015.02.012
Yue X, Zhang H (2020) A multi-level image thresholding approach using Otsu based on the improved invasive weed optimization algorithm. Sig Imag Video Proc 14:575–582. https://doi.org/10.1007/s11760-019-01585-3
Zhou Y, Yang X, Ling Y, Zhang J (2018) Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 77:23699–23727. https://doi.org/10.1007/s11042-018-5637-x
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest to report regarding the present study.
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.
About this article
Cite this article
Renugambal, A., Bhuvaneswari, K.S. & Tamilarasan, A. Hybrid SCCSA: An efficient multilevel thresholding for enhanced image segmentation. Multimed Tools Appl 82, 32711–32753 (2023). https://doi.org/10.1007/s11042-023-14637-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14637-1