Circuits, Systems, and Signal Processing

, Volume 38, Issue 7, pp 3058–3106 | Cite as

A Water Cycle Algorithm-Based Multilevel Thresholding System for Color Image Segmentation Using Masi Entropy

  • Pankaj Kandhway
  • Ashish Kumar BhandariEmail author


In this paper, a recently developed metaheuristic water cycle algorithm (WCA) is coupled with Masi entropy (Masi-WCA) to perform color image segmentation over the optimal threshold value selection process. Masi entropy gives the non-extensive/additive information that exists in an image by a tunable entropic parameter. The water cycle algorithm is a newly established population-based method which has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. The idea behind WCA is grounded on thought of water cycle and how streams and rivers flow downward toward the sea in the real world. The key feature of this paper is to exploit the modern optimization techniques such as water cycle algorithm, monarch butterfly optimization, grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and wind-driven optimization for the color image segmentation purpose. In this paper, two objective (fitness) functions are exploited which are Tsallis and Masi entropy for a fair comparison of the proposed method. The proposed scheme is examined intensively regarding quality, and a statistical graph is included to compare the outcomes of the proposed Masi-WCA method against similar algorithms. Different to other recently developed optimization algorithms used for color image multilevel thresholding operations, WCA presents a better performance in terms of superior quality and fast convergence rate. Experimental evidence encourages the use of WCA for multilevel thresholding with Masi entropy, while it concludes that Tsallis entropy does not outperform over the proposed scheme.


Multilevel thresholding Masi entropy Tsallis entropy Water cycle algorithm Grasshopper optimization Monarch butterfly optimization Color image segmentation 


  1. 1.
    A.K. Bhandari, A. Kumar, G.K. Singh, SVD based poor contrast improvement of blurred multispectral remote sensing satellite images, in Computer and Communication Technology (ICCCT), 2012 Third International Conference on, (IEEE, 2012), pp. 156–159Google Scholar
  2. 2.
    A.K. Bhandari, A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Comput. Appl. (2018).
  3. 3.
    A.K. Bhandari, S. Maurya, A.K. Meena, Social spider optimization based optimally weighted otsu thresholding for image enhancement. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2018).
  4. 4.
    Z. Bayraktar, M. Komurcu, J.A. Bossard, D.H. Werner, The wind driven optimization technique and its application in electromagnetics. IEEE Trans. Antennas Propag. 61(5), 2745–2757 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    A.K. Bhandari, A. Kumar, G.K. Singh, Feature extraction using normalized difference vegetation index (NDVI): a case study of Jabalpur city. Procedia Technol. 6, 612–621 (2012)CrossRefGoogle Scholar
  6. 6.
    A.K. Bhandari, V.K. Singh, A. Kumar, G.K. Singh, Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst. Appl. 41(7), 3538–3560 (2014)CrossRefGoogle Scholar
  7. 7.
    A.K. Bhandari, V. Soni, A. Kumar, G.K. Singh, Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD. Int. J. Remote Sens. 35(5), 1601–1624 (2014)CrossRefGoogle Scholar
  8. 8.
    A.K. Bhandari, V. Soni, A. Kumar, G.K. Singh, Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD. ISA Trans. 53(4), 1286–1296 (2014)CrossRefGoogle Scholar
  9. 9.
    A.K. Bhandari, A. Kumar, G.K. Singh, Improved knee transfer function and gamma correction based method for contrast and brightness enhancement of satellite image. AEU Int. J. Electron. Commun. 69(2), 579–589 (2015)CrossRefGoogle Scholar
  10. 10.
    A.K. Bhandari, A. Kumar, G.K. Singh, Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst. Appl. 42(3), 1573–1601 (2015)CrossRefGoogle Scholar
  11. 11.
    A.K. Bhandari, A. Kumar, G.K. Singh, Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Syst. Appl. 42(22), 8707–8730 (2015)CrossRefGoogle Scholar
  12. 12.
    A.K. Bhandari, D. Kumar, A. Kumar, G.K. Singh, Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm. Neurocomputing 174, 698–721 (2016)CrossRefGoogle Scholar
  13. 13.
    A.K. Bhandari, A. Kumar, S. Chaudhary, G.K. Singh, A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms. Expert Syst. Appl. 63, 112–133 (2016)CrossRefGoogle Scholar
  14. 14.
    A.K. Bhandari, A. Kumar, G.K. Singh, V. Soni, Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold. J. Exp. Theor. Artif. Intell. 28(1–2), 71–95 (2016)CrossRefGoogle Scholar
  15. 15.
    A.K. Bhandari, A. Kumar, S. Chaudhary, G.K. Singh, A new beta differential evolution algorithm for edge preserved colored satellite image enhancement. Multidimens. Syst. Signal Process. 28(2), 495–527 (2017)zbMATHCrossRefGoogle Scholar
  16. 16.
    A.K. Bhandari, A. Kumar, G.K. Singh, V. Soni, Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD. Multidimens. Syst. Signal Process. 27(2), 453–476 (2016)CrossRefGoogle Scholar
  17. 17.
    P. Civicioglu, Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219(15), 8121–8144 (2013)MathSciNetzbMATHGoogle Scholar
  18. 18.
    H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi, Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 110, 151–166 (2012)CrossRefGoogle Scholar
  19. 19.
    L. He, S. Huang, Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240, 152–174 (2017)CrossRefGoogle Scholar
  20. 20.
    A.B. Ishak, Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework. Phys. A 466, 521–536 (2017)CrossRefGoogle Scholar
  21. 21.
    J. Kittler, J. Illingworth, Minimum error thresholding. Pattern Recogn. 19(1), 41–47 (1986)CrossRefGoogle Scholar
  22. 22.
    A. Kumar, A.K. Bhandari, P. Padhy, Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing. IET Signal Proc. 6(7), 617–625 (2012)MathSciNetCrossRefGoogle Scholar
  23. 23.
    C.H. Li, C.K. Lee, Minimum cross entropy thresholding. Pattern Recogn. 26(4), 617–625 (1993)CrossRefGoogle Scholar
  24. 24.
    X. Li, J. Wang, A steganographic method based upon JPEG and particle swarm optimization algorithm. Inf. Sci. 177(15), 3099–3109 (2007)CrossRefGoogle Scholar
  25. 25.
    Y. Li, X. Bai, L. Jiao, Y. Xue, Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl. Soft Comput. 56, 345–356 (2017)CrossRefGoogle Scholar
  26. 26.
    Y.W. Lim, S.U. Lee, On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recogn. 23(9), 935–952 (1990)CrossRefGoogle Scholar
  27. 27.
    M. Masi, A step beyond Tsallis and Rényi entropies. Phys. Lett. A 338(3–5), 217–224 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    S. Mishra, M. Panda, Bat algorithm for multilevel colour image segmentation using entropy-based thresholding. Arab. J. Sci. Eng. 43, 7285–7314 (2018)CrossRefGoogle Scholar
  29. 29.
    F. Nie, P. Zhang, J. Li, D. Ding, A novel generalized entropy and its application in image thresholding. Signal Process. 134, 23–34 (2017)CrossRefGoogle Scholar
  30. 30.
    D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, V. Osuna, A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 139, 357–381 (2014)CrossRefGoogle Scholar
  31. 31.
    S. Pare, A.K. Bhandari, A. Kumar, G.K. Singh, An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix. Expert Syst. Appl. 87, 335–362 (2017)CrossRefGoogle Scholar
  32. 32.
    S. Pare, A.K. Bhandari, A. Kumar, V. Bajaj, Backtracking search algorithm for color image multilevel thresholding. SIViP 12(2), 385–392 (2018)CrossRefGoogle Scholar
  33. 33.
    S. Pare, A.K. Bhandari, A. Kumar, G.K. Singh, A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm. Comput. Electr. Eng. 70, 476–495 (2018)CrossRefGoogle Scholar
  34. 34.
    T. Pun, A new method for grey-level picture thresholding using the entropy of the histogram. Signal Process. 2(3), 223–237 (1980)CrossRefGoogle Scholar
  35. 35.
    A.K. Bhandari, A. Kumar, G.K. Singh. Improved feature extraction scheme for satellite images using NDVI and NDWI technique based on DWT and SVD. Arab. J. Geosci. 8(9), 6949–6966 (2015)CrossRefGoogle Scholar
  36. 36.
    S. Pare, A.K. Bhandari, A. Kumar, G.K. Singh, Rényi’s entropy and Bat algorithm based color image multilevel thresholding, in Machine Intelligence and Signal Analysis, ed. by M. Tanveer, R. Pachori (Springer, Singapore 2019), pp. 71–84CrossRefGoogle Scholar
  37. 37.
    A. K. Bhandari, M. Gadde, A. Kumar, G. K. Singh, Comparative analysis of different wavelet filters for low contrast and brightness enhancement of multispectral remote sensing images. in 2012 International Conference on Machine Vision and Image Processing (MVIP), Taipei (2012), pp. 81–86Google Scholar
  38. 38.
    S. Pare, A.K. Bhandari, A. Kumar, G.K. Singh, S. Khare, Satellite image segmentation based on different objective functions using genetic algorithm: a comparative study, in Digital Signal Processing (DSP), 2015 IEEE International Conference on. (IEEE 2015), pp. 730–734Google Scholar
  39. 39.
    P.K. Sahoo, G. Arora, Image thresholding using two-dimensional Tsallis–Havrda–Charvát entropy. Pattern Recogn. Lett. 27(6), 520–528 (2006)CrossRefGoogle Scholar
  40. 40.
    P. Sahoo, C. Wilkins, J. Yeager, Threshold selection using Renyi’s entropy. Pattern Recogn. 30(1), 71–84 (1997)zbMATHCrossRefGoogle Scholar
  41. 41.
    S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)CrossRefGoogle Scholar
  42. 42.
    M. Sezgin, B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–166 (2004)CrossRefGoogle Scholar
  43. 43.
    V. Soni, A.K. Bhandari, A. Kumar, G.K. Singh, Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms. IET Signal Proc. 7(8), 720–730 (2013)CrossRefGoogle Scholar
  44. 44.
    W.H. Tsai, Moment-preserving thresolding: a new approach. Comput. Vis. Graph. Image Process. 29(3), 377–393 (1985)CrossRefGoogle Scholar
  45. 45.
    Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  46. 46.
    G.-G. Wang, S. Deb, Z. Cui, Monarch butterfly optimization. Neural Comput. Appl. (2015). CrossRefGoogle Scholar
  47. 47.
    X.S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). (Springer, Berlin, Heidelberg, 2010), pp. 65–74Google Scholar
  48. 48.
    L. Zhang, L. Zhang, X. Mou, D. Zhang, FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology PatnaPatnaIndia

Personalised recommendations