SpringerLink
Log in

Multilevel thresholding for image segmentation with exchange market algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image segmentation is the prime factor to elicit the detailed investigation of an image. The desired information from an image can be easily obtained through the intuitive technique called thresholding. In this technique, detailed analysis of different classes of an image is realised through Multilevel Thresholding (MLT). Accurate and ideal threshold values achieved by non-parametric objective functions such as Tsallis and Renyi are briefed in this paper. The non-additive property of Tsallis and entropic threshold selection property of Renyi drive to search the global threshold value precisely. Higher the segmentation level more is the computational time for exploration of optimal threshold with Tsallis and Renyi. This challenge is countered by MLT based Tsallis and Renyi, aided with Exchange Market Algorithm (EMA). Several research on nature- inspired algorithms such as Bacterial Foraging (BF), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are carried out. For the first time, this paper proposes a powerful metaheuristic EMA technique for image segmentation, which implements the strategies of shareholders in stable and unstable mode to earn profit. The cognizance of the shareholders is extracted to attain the desired goal to reach the global threshold avoiding premature convergence. Empirical outcome of the results indicate that outstanding tuning search is achieved by EMA compared to extensive search techniques such as PSO, BF and GA. Exploration and exploitation assessment by metrics such as stability, computational efficiency, Peak Signal to Noise Ratio (PSNR), uniformity measure and Wilcoxon rank sum test affirm the Tsallis and Renyi based EMA surpass the existing techniques to analyse the real-world images.

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

Access this article

Log in via an institution

Price includes VAT (Germany)

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Baby Reshma K P, Madhu S, Nair S (2018) Multilevel Thresholding for image segmentation using krill herd optimisation algorithm. J King Sand Univ Comput Inf Sci, https://doi.org/10.1016/j.jksuci.2018.04.007.

  2. Bhandari AK, Kumar A, Chaudhary S, Singh GK (2016) A novel color image multilevel thresholding-based segmentation using nature inspired optimization algorithms. Expert Syst Appl 63:112–133. https://doi.org/10.1016/j.eswa.2016.06.044

    Article  Google Scholar 

  3. Bhandari AK, Kumar A, Singh GK (2015) Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Syst Appl 42:8707–8730. https://doi.org/10.1016/j.eswa.2015.07.025

    Article  Google Scholar 

  4. Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization-based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41:3538–3560. https://doi.org/10.1016/j.eswa.2013.10.059

    Article  Google Scholar 

  5. Borjigin S, Sahoo PK (2019) Color image segmentation based on multi-level Tsallis-Havrda-Charvát entropy and 2D histogram using PSO algorithms. Pattern Recogn 92:107–118. https://doi.org/10.1016/j.patcog.2019.03.011

    Article  Google Scholar 

  6. Elaziz M, Oliva D, Ewees AX (2019) Multi-level Thresholding-based Grey scale image segmentation using multi-objective multi-verse optimizer. Expert Syst Appl 125:112–129

    Article  Google Scholar 

  7. Fu KS, Mui JK (1981) A survey on image segmentation. Pattern Recogn 13(1):3–16. https://doi.org/10.1016/0031-3203(81)90028-5

    Article  MathSciNet  Google Scholar 

  8. Gao H, Fu Z, Pun CM, Hu L, Lan R (2017) 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 

  9. Gao H, Xu W, Sun J, Tang Y (2010) Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE Trans Instrum Meas 59:934–946. https://doi.org/10.1109/tim.2009.2030931

    Article  Google Scholar 

  10. Ghorbani N (2016) Combined heat and power economic dispatch using exchange market algorithm. Int J Electr Power Energy Syst 82:58–66. https://doi.org/10.1016/j.ijepes.2016.03.004

    Article  Google Scholar 

  11. Ghorbani N, Babaei E (2014) Exchange market algorithm. Appl Soft Comput 19:177–187. https://doi.org/10.1016/j.asoc.2014.02.006

    Article  Google Scholar 

  12. Ghorbani N, Babaei E (2016) Exchange market algorithm for economic load dispatch. Electr Power Energy Syst 75:19–27. https://doi.org/10.1016/j.ijepes.2015.08.013

    Article  Google Scholar 

  13. Ghorbani N, Babaei E (2017) The exchange market algorithm with smart searching for solving economic dispatch problems. Int J Manag Sci Eng Manag 13(3):175–187. https://doi.org/10.1080/17509653.2017.1365262

    Article  Google Scholar 

  14. Ghorbani N, Babaei E, Sadkoglu F (2017) Binary exchange market algorithm. Procedia Comput Sci 120:633–640. https://doi.org/10.1016/j.procs.2017.11.292

    Article  Google Scholar 

  15. Han J, Yang C, Zhou X, Gui W (2017) A new multi-threshold image segmentation approach using state transition algorithm. Appl Math Model 44:588–601. https://doi.org/10.1016/j.apm.2017.02.015

    Article  MathSciNet  MATH  Google Scholar 

  16. 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 

  17. Ishak AB (2017) A two- dimensional multilevel thresholding method for image segmentation. Appl Soft Comput 52:306–322. https://doi.org/10.1016/j.asoc.2016.10.034

    Article  Google Scholar 

  18. Jiang Y, Yeh WC, Hao Z, Yang Z (2016) A cooperative honeybee mating algorithm and its application in multi-level threshold image segmentation. Inf Sci 369:171–183. https://doi.org/10.1016/j.ins.2016.06.020

    Article  Google Scholar 

  19. Joafi WT (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 

  20. Kurban T, Civicioglu P, Kurban R, Besdok E (2014) Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl Soft Comput 23:128–143. https://doi.org/10.1016/j.asoc.2014.05.037

    Article  Google Scholar 

  21. Lei B, Fan J (2020) Multilevel minimum cross entropy thresholding: a comparative study. Appl Soft Comput 96:106588. https://doi.org/10.1016/j.asoc.2020.106588

    Article  Google Scholar 

  22. 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 

  23. Li J, Tang W, Wang J, Zhang X (2019) A multilevel color image thresholding scheme based on minimum cross entropy and alternating direction method of multipliers. Optik 183:30–37. https://doi.org/10.1016/j.ijleo.2019.02.004

    Article  Google Scholar 

  24. Malakar U, Potocnik 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

    Article  Google Scholar 

  25. Mittal H, Saraswat M (2018) An optimal multilevel thresholding segmentation using non-local means 2D histogram and exponential K-best gravitational search algorithm. Eng Appl Artif Intell 71:226–235. https://doi.org/10.1016/j.engappai.2018.03.001

    Article  Google Scholar 

  26. Pal NR, Pal SK (1993) A review on image segmentation. Pattern Recogn 26:1277–1294. https://doi.org/10.1016/0031-3203(93)90135-J

    Article  Google Scholar 

  27. Pare S, Bhandari AK, Kumar A, Singh GK (2017) A new technique for multilevel color image thresholding based on modified fuzzy entropy and levy flight firefly algorithm. Comput Electr Eng 70:476–495. https://doi.org/10.1016/j.compeleceng.2017.08.008

    Article  Google Scholar 

  28. Pare S, Bhandari AK, Kumar A, Singh GK (2017) An optimal colour image multilevel thresholding technique using Grey-level co- occurrence matrix. Expert Syst Appl 87:335–362. https://doi.org/10.1016/j.eswa.2017.06.021

    Article  Google Scholar 

  29. Pare S, Kumar A, Bajaj V, Singh GK (2017) An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy. Appl Soft Comput 61:570–592

    Article  Google Scholar 

  30. Portes de Albuquerque M, Esquef IA, Gesualdi Mello AR, Portes de Albuquerque M (2004) Image thresholding using Tsallis entropy. Pattern Recogn Lett 25:1059–1065. https://doi.org/10.1016/j.patrec.2004.03.003

    Article  Google Scholar 

  31. Rajan A, Malakar T (2016) Optimum economic and emission dispatch using exchange market algorithm. Int J Electr Power Energy Syst 82:545–560. https://doi.org/10.1016/j.ijepes.2016.04.022

    Article  Google Scholar 

  32. Sarkar S, Das S, Chaudhuri S (2014) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn Lett 54:27–35. https://doi.org/10.1016/j.patrec.2014.11.009

    Article  Google Scholar 

  33. Sarkar S, Das S, Chaudhuri S (2016) Hyper-spectral image segmentation using Rényi entropy based multi-level Thresholding aided with differential evolution. Expert Syst Appl 50:120–129

    Article  Google Scholar 

  34. Sathya PD, Kayalvizhi R (2011) Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Eng Appl Artif Intell 24:595–615. https://doi.org/10.1016/j.engappai.2010.12.001

    Article  Google Scholar 

  35. Sathya P D, Sakthivel V P (2013) Multilevel Renyi entropy threshold selection based on bacterial foraging algorithm. Recent advancements in system modelling applications, lecture notes in electrical Engineering, pp 53-65, https://doi.org/10.1007/978-81-322-1035-1_6.

  36. Singh Gill H, Singh Khehra B, Singh A, Kaur L (2018) Teaching-learning-based optimization algorithm to minimize cross entropy for selecting multilevel threshold values. Egypt Inf J 20:11–25. https://doi.org/10.1016/j.eij.2018.03.006

    Article  Google Scholar 

  37. Sohrabi F, Nazari-Heris M, Mohammadi-Ivatloo B, Asadi S (2018) Optimal chiller loading for saving energy by exchange market algorithm. Energy Build 169:245–253. https://doi.org/10.1016/j.enbuild.2018.03.077

    Article  Google Scholar 

  38. Srikanth R, Bikshalu K (2020) 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 

  39. Sun G, Zhang A, Yao Y, Wang Z (2016) A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding. Appl Soft Comput 46:703–730. https://doi.org/10.1016/j.asoc.2016.01.054

    Article  Google Scholar 

  40. Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184–209. https://doi.org/10.1016/j.eswa.2016.03.032

    Article  Google Scholar 

  41. Wang S, Chung FL (2005) Note on the equivalence relationship between Rényi-entropy based and Tsallis-entropy based image thresholding. Pattern Recogn Lett 26(14):2309–2312. https://doi.org/10.1016/j.patrec.2005.03.027

    Article  Google Scholar 

  42. Wilcoxon F (1945) Individual comparisons by ranking methods. Int Biom Soc 1(6):80–83

    Google Scholar 

  43. Wunnava A, Kumar Naik M, Panda R, Jena B, Abraham A (2020) A differential evolutionary adaptive Harris hawks optimization for two dimensional practical Masi entropy-based multilevel image thresholding. J King Saud Univ Comput Inf Sci https://doi.org/10.1016/j.jksuci.2020.05.001

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Research Scholar, Annamalai University, Chidambaram, 608002, TamilNadu, India

    R. Kalyani

  2. Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, 608002, TamilNadu, India

    P. D. Sathya

  3. Department of Electrical and Electronics Engineering, Govt College of Engineering, Dharmapuri, 636704, TamilNadu, India

    V. P. Sakthivel

Authors
  1. R. Kalyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. D. Sathya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. P. Sakthivel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Kalyani.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kalyani, R., Sathya, P.D. & Sakthivel, V.P. Multilevel thresholding for image segmentation with exchange market algorithm. Multimed Tools Appl 80, 27553–27591 (2021). https://doi.org/10.1007/s11042-021-10909-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10909-w

Keywords

Search

Navigation