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A hybrid equilibrium optimizer algorithm for multi-level image segmentation

  • Received: 04 April 2021 Accepted: 20 May 2021 Published: 27 May 2021
  • Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

    Citation: Hong Qi, Guanglei Zhang, Heming Jia, Zhikai Xing. A hybrid equilibrium optimizer algorithm for multi-level image segmentation[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4648-4678. doi: 10.3934/mbe.2021236

    Related Papers:

  • Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.



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    [1] 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 (2017), 345-356. doi: 10.1016/j.asoc.2017.03.018
    [2] M. A. E. Aziz, A. A. Ewees, A. E. Hassanien, Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation, Expert Syst. Appl., 83 (2017), 242-256. doi: 10.1016/j.eswa.2017.04.023
    [3] B. Leszczyński, A. Gancarczyk, A. Wróbel, M. Piatek, J. Lojewska, A. Kolodziej, et al., Global and local thresholding methods applied to X-ray microtomographic analysis of Metallic Foams, J. Nondestruct. Eval., 35 (2016), 35-35.
    [4] K. Somkantha, N. Theera-Umpon, S. Auephanwiriyakul, Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features, IEEE Trans. Biomed. Eng., 58 (2011), 567-573. doi: 10.1109/TBME.2010.2091129
    [5] T. H. Farag, W. A. Hassan, H. A. Ayad, A. S. AlBahussain, U. A. Badawi, M. K. Alsmadi, Extended absolute fuzzy connectedness segmentation algorithm utilizing region and boundary-based information, Arab. J. Sci. Eng., 42 (2017), 3573-3583. doi: 10.1007/s13369-017-2577-0
    [6] S. Niu, C. Qiang, L. D. Sisternes, Z. Ji, Z. Zhou, D. Rubin, Robust noise region-based active contour model via local similarity factor for image segmentation, Pattern Recognit., 61 (2017), 104-119. doi: 10.1016/j.patcog.2016.07.022
    [7] L. Fan, D. A. Clausi, L. Xu, A. Wong, ST-IRGS: A region-based self-training algorithm applied to hyperspectral image classification and segmentation, IEEE Trans. Geosci. Remote Sensing, 56 (2018), 3-16. doi: 10.1109/TGRS.2017.2713123
    [8] X. Cheng, C. M. Shuai, J. Wang, W. Li, J. Shuai, Y. Liu, Building a sustainable development model for China's poverty-stricken reservoir regions based on system dynamics, J. Clean Prod., 176 (2018), 535-554. doi: 10.1016/j.jclepro.2017.12.068
    [9] S. Dong, H. Li, J. Wang, X. Zhang, X. Ji, Improved flexible Li-ion hybrid capacitors: Techniques for superior stability, Nano Res., 10 (2017), 4448-4456. doi: 10.1007/s12274-017-1753-6
    [10] K. S. Hong, M. J. Khan, Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: A review, Front. Neurorobot., 11 (2017), 35.
    [11] T. Lv, G. Yang, Y. Zhang, Y. Zhang, J. Yang, Y. Chen, Vessel segmentation using centerline constrained level set method, Multimed. Tools Appl., 78 (2019), 17051-17075. doi: 10.1007/s11042-019-07944-z
    [12] Y. Chen, Y. Zhang, J. Yang, Curve-like structure extraction using minimal path propagation with backtracing, IEEE Trans. Image Process, 25 (2016), 988-1003. doi: 10.1109/TIP.2015.2496279
    [13] L. Ngo, J. Cha, J. H. Han, Deep neural network regression for automated retinal layer segmentation in optical coherence tomography images, IEEE Trans. Image Process, 29 (2020), 303-312. doi: 10.1109/TIP.2019.2931461
    [14] C. Guan, S. Wang, W. C. Liew, Lip image segmentation based on a fuzzy convolutional neural network, IEEE Trans. Fuzzy Syst., 28 (2019), 1242-1251.
    [15] A. M. Bensaid, L. Hal, J. Bezdek, L. Clarke, M. Silbiger, J. Arrington, R. Murtagh, Validity-guided (re)clustering with applications to image segmentation, IEEE Trans. Fuzzy Syst., 4 (1996), 112-123. doi: 10.1109/91.493905
    [16] N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man. Cybern. -Syst., 9 (2007), 62-66.
    [17] W. Hussein, S. Sahran, S. Abdullah, A fast scheme for multilevel thresholding based on a modified bees algorithm, Knowledge-Based Syst., 101 (2016), 114-134. doi: 10.1016/j.knosys.2016.03.010
    [18] J. Liu, W. Li, Y. Tian, Automatic thresholding of gray-level pictures using two-dimension Otsu method, 1991 International Conference on Circuits and Systems, (1991), 325-327.
    [19] X. J. Jing, J. F. Li, Y. L. Liu, Image segmentation based on 3-D maximum between-cluster variance, Acta Elect, Acta. Electronica. Sinica., 31 (2003), 1281-1285.
    [20] L. Wang, H. Duan, J. Wang, A fast algorithm for three-dimensional Otsu's thresholding method, 2008 IEEE International Symposium on IT in Medicine and Education, IEEE, 2008,136-140.
    [21] L. Bian, G. Huo, Q. Li, Multi-threshold MRI image segmentation algorithm based on Curevelet transformation and multi-objective particle swarm optimization, J. Comp. Appl., 36 (2016), 3188-3195.
    [22] N. Muangkote, K. Sunat, S. Chiewchanwattana, Multilevel thresholding for satellite image segmentation with moth-flame based optimization, International Joint Conference on Computer Science & Software Engineering, IEEE, (2016).
    [23] S. Borjigin, P. Sahoo, Color Image Segmentation based on multi-level Tsallis-Havrda-Charvát entropy and 2D histogram using PSO Algorithms, Pattern Recognit., 92 (2019), 107-118. doi: 10.1016/j.patcog.2019.03.011
    [24] A. Wunnava, M. Naik, R. Panda, B. Jena, A. Abraham, An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding, Appl. Soft Comput., 95 (2020), 106526.
    [25] A. Bhandari, K. Rahul, A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm, Infrared Phys. Technol., 98 (2019), 132-154. doi: 10.1016/j.infrared.2019.03.010
    [26] A. Koshki, M. Zekri, R. Ahmadzadeh, S. Sadri, E. Mahmoudzadeh, Extending contour level set model for multi-class image segmentation with Application to Breast Thermography Images, Infrared Phys. Technol., 105 (2020), 103174.
    [27] D. Yousri, M. A. Elaziz, S. Mirjalili, Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation, Knowledge-Based Syst., 197 (2020), 105889-105894. doi: 10.1016/j.knosys.2020.105889
    [28] M. Marinaki, Y. Marinakis, A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands, Expert Syst. Appl., 46 (2016), 145-163. doi: 10.1016/j.eswa.2015.10.012
    [29] H. Bouchekara, A. Chaib, M. Abido, R. Sehiemy, Optimal power flow using an Improved Colliding Bodies Optimization algorithm, Appl. Soft Comput., 42 (2016), 119-131. doi: 10.1016/j.asoc.2016.01.041
    [30] A. E. Smith, Multi-objective optimization using evolutionary algorithms, IEEE Trans. Evol. Comput., 6 (2002), 526-526. doi: 10.1109/TEVC.2002.804322
    [31] D. E. Goldberg, J. H. Holland, Genetic algorithms and machine learning, Mach. Learn., 3 (1988), 95-99.
    [32] R. Storn, K. Price, Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim., 11 (1997), 341-359. doi: 10.1023/A:1008202821328
    [33] J. Kennedy, R. Eberhart, Particle swarm optimization, Proceedings of ICNN'95—International Conference on Neural Networks, IEEE, 2002.
    [34] M. Seyedali, The Ant Lion Optimizer, Adv. Eng. Softw., 83 (2015), 80-98.
    [35] A. Askarzadeh, A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Comput. Struct., 169 (2016), 1-12. doi: 10.1016/j.compstruc.2016.03.001
    [36] T. Biyanto, Matradji, S. Irawan, H. Febrianto, N. Afdanny, A. Rahman, et al, Killer Whale algorithm: An algorithm inspired by the life of Killer Whale, Proced. Computer Sci., 124 (2017), 151-157.
    [37] Z. Geem, J. H. Kim, G. V. Loganathan, A new heuristic optimization algorithm: Harmony search, Simulation, 76 (2001), 60-68. doi: 10.1177/003754970107600201
    [38] Y. Zheng, Water wave optimization: A new nature-inspired metaheuristic, Comput. Oper. Res., 55 (2015), 1-11. doi: 10.1016/j.cor.2014.10.008
    [39] A. Faramarzi, M. Heidarinejad, B. Strphens, S. Mirjalili, Equilibrium optimizer algorithm: A novel meta-heuristic optimization algorithm, Adv. Eng. Software, 191 (2020), 105190.
    [40] D. H. Wolpert, W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67-82. doi: 10.1109/4235.585893
    [41] H. Xu, W. Liang, Q. Gao, A self-gap-correction method for accurate permittivity measurement using the hybrid optimization algorithm, IEEE Trans. Instrum. Meas., 68 (2019), 1781-1787. doi: 10.1109/TIM.2019.2896874
    [42] P. Upadhyay, J. K. Chhabra, Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm, J. Ambient Intell. Humaniz. Comput., 12 (2021), 1081-1098. doi: 10.1007/s12652-020-02143-3
    [43] A. Kaur, C. Singh, SAR image segmentation based on hybrid PSOGSA optimization algorithm, Int. J. Eng. Res. Appl., 4 (2014), 5-11.
    [44] D. Kole, A. Halder, An efficient dynamic image segmentation algorithm using a hybrid technique based on particle swarm optimization and genetic algorithm, International Conference on Advances in Computer Engineering, IEEE, 2010.
    [45] H. Gao, C. M. Pun, S. Kwong, An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy, Inf. Sci., 369 (2016), 500-521. doi: 10.1016/j.ins.2016.07.017
    [46] M. H. Mozaffari, W. S. Lee, Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation, IET Image Process., 11 (2017), 605-619. doi: 10.1049/iet-ipr.2016.0489
    [47] A. Bhandari, I.V. Kumar, K. Srinivas, Cuttlefish Algorithm-Based Multilevel 3-D Otsu Function for Color Image Segmentation, IEEE Trans. Instrum. Meas., 69 (2020), 1871-1880. doi: 10.1109/TIM.2019.2922516
    [48] S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: Theory and application, Adv. Eng. Softw., 105 (2017), 30-47. doi: 10.1016/j.advengsoft.2017.01.004
    [49] Y. Sun, J. Wei, T. Wu, K. Xiao, J. Bao, Y. Jin, Brain storm optimization using a slight relaxation selection and multi-population based creating ideas ensemble, Appl. Intell., 50 (2020), 3137-3161. doi: 10.1007/s10489-020-01690-8
    [50] D. Oliva, S. Hinojosa, E. Cuevas, G. Pajares, O. Avalos, J. Gálvez, Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm, Expert Syst. Appl., 79 (2017), 164-180. doi: 10.1016/j.eswa.2017.02.042
    [51] A. Y. Abdelaziz, E. S. Ali, S. A. Elazim, Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems, Energy, 101 (2016), 506-518. doi: 10.1016/j.energy.2016.02.041
    [52] T. P. Xuan, P. Siarry, H. Oulhadj, Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation, Appl. Soft Comput., 65 (2018), 230-242. doi: 10.1016/j.asoc.2018.01.003
    [53] Z. W. Ye, M. W. Wang, W. Liu, S. Chen, Fuzzy entropy based optimal thresholding using bat algorithm, Appl. Soft Comput., 31 (2015), 381-395. doi: 10.1016/j.asoc.2015.02.012
    [54] D. E. Dutkay, C. K. Lai, Uniformity of measures with Fourier frames, Adv. Math., 252 (2014), 684-707. doi: 10.1016/j.aim.2013.11.012
    [55] K.G. Lore, A. Akintayo, S. Sarkar, LLNet: A deep autoencoder approach to natural low-light image enhancement, Pattern Recogn., 61 (2017), 650-662. doi: 10.1016/j.patcog.2016.06.008
    [56] M. Koppel, K. Muller, T. Wiegand, Filling disocclusions in extrapolated virtual views using hybrid texture synthesis, IEEE Trans. Broadcast., 62 (2016), 1-13. doi: 10.1109/TBC.2015.2470134
    [57] H. M. Jia, Z. K. Xing, W. L. Song, Three dimensional pulse coupled neural network based on hybrid optimization algorithm for oil pollution image segmentation, Remote Sens., 11 (2019), 1046.
    [58] A. Ewees, M. Elaziz, D. Oliva, Image segmentation via multilevel thresholding using hybrid optimization algorithms, J. Electron. Imag., 27 (2018), 1-26.
    [59] H. M. Jia, C. Lang, D. Oliva, S. Song, Dynamic harris hawks optimization with mutation mechanism for satellite image segmentation, Remote Sens., 11 (2019), 1421.
    [60] M. Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J. Am. Stat. Assoc., 32 (1939), 675-701.
    [61] B. Rosner, R. J. Glynn, M. Lee, Incorporation of clustering effects for the Wilcoxon rank sum test: A large-sample approach, Biometrics, 59 (2003), 1089-1098. doi: 10.1111/j.0006-341X.2003.00125.x
    [62] Y. Chen, S. K. Park, Y. Ma, A. Rajeshkanna, A new automatic parameter setting method of a simplified PCNN for image segmentation, IEEE Trans. Neural Netw. Learn. Syst., 22 (2011), 880-892. doi: 10.1109/TNN.2011.2128880
    [63] E. Rajaby, S. M. Ahadi, H. Aghaeinia, Robust color image segmentation using fuzzy c-means with weighted hue and intensity, Digit. Signal Prog., 51 (2016), 170-183. doi: 10.1016/j.dsp.2016.01.010
    [64] Z. K. Xing, H. M. Jia, Multilevel color image segmentation based on GLCM and improved Salp swarm algorithm, IEEE Access, 7 (2019), 37672-37690. doi: 10.1109/ACCESS.2019.2904511
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