Skip to main content
Springer Nature Link
Log in

A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Two-dimensional (2D) multilevel thresholding is an important technique for noisy image segmentation which has drawn much attention during the past few years. The conventional image segmentation methods are efficient for 2D bi-level thresholding. However, the computational complexity grows exponentially when extended to 2D multilevel thresholding since they search the optimal thresholds by exhaustive strategy. To tackle this problem, a fuzzy adaptive gravitational search algorithm (FAGSA) using Tsallis entropy as its objective function has been presented to find the optimal 2D multilevel thresholds in this paper. In the FAGSA, fuzzy logic controllers are designed to tune the control parameters. The state-of-the-art heuristic algorithms are compared with this proposed algorithm. Both test images and noisy images are utilized in the experiments to evaluate the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our algorithm in terms of the objective function value, image quality measures and time consumption.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abdel-Khalek S, Ishak AB, Omer OA et al (2017) A two-dimensional image segmentation method based on genetic algorithm and entropy[J]. Optik 131:414–422

    Article  Google Scholar 

  • Agrawal S, Panda R, Bhuyan S et al (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm[J]. Swarm Evol Comput 11:16–30

    Article  Google Scholar 

  • Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  • Beigvand SD, Abdi H, La Scala M (2016) Combined heat and power economic dispatch problem using gravitational search algorithm[J]. Electr Power Syst Res 133:160–172

    Article  Google Scholar 

  • Bhandari AK, Singh VK, Kumar A et al (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy[J]. Expert Syst Appl 41(7):3538–3560

    Article  Google Scholar 

  • 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[J]. Expert Syst Appl 42(3):1573–1601

    Article  Google Scholar 

  • Bhandari AK, Kumar A, Chaudhary S et al (2016) A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms[J]. Expert Syst Appl 63:112–133

    Article  Google Scholar 

  • Chen LC, Papandreou G, Kokkinos I et al (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  • Cheriet M, Said JN, Suen CY (1998) A recursive thresholding technique for image segmentation[J]. IEEE Trans Image Process 7(6):918–921

    Article  Google Scholar 

  • Doraghinejad M, Nezamabadi-pour H (2014) Black hole: a new operator for gravitational search algorithm. Int J Comput Intell Syst 7(5):809–826

    Article  Google Scholar 

  • Duman S, Güvenç U, Sönmez Y et al (2012) Optimal power flow using gravitational search algorithm[J]. Energy Convers Manage 59:86–95

    Article  Google Scholar 

  • El Aziz MA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation[J]. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  • Erwin S, Saputri W (2018) Hybrid multilevel thresholding and improved harmony search algorithm for segmentation[J]. Int J Electr Comput Eng (IJECE) 8(6):4593–4602

    Article  Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH et al (2013) Bat algorithm for constrained optimization tasks[J]. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  • Han X, Chang X (2012) A chaotic digital secure communication based on a modified gravitational search algorithm filter. Inf Sci 208:14–27

    Article  Google Scholar 

  • He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation[J]. Neurocomputing 240:152–174

    Article  Google Scholar 

  • Horng MH (2011) Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation[J]. Expert Syst Appl 38(11):13785–13791

    Google Scholar 

  • Ishak AB (2017a) A two-dimensional multilevel thresholding method for image segmentation[J]. Appl Soft Comput 52:306–322

    Article  Google Scholar 

  • Ishak AB (2017b) Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework[J]. Phys A 466:521–536

    Article  Google Scholar 

  • Kang K, Bae C, Yeung HWF et al (2018) A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization[J]. Appl Soft Comput 66:319–329

    Article  Google Scholar 

  • Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation[J]. Expert Syst Appl 86:64–76

    Article  Google Scholar 

  • Kumar Y, Sahoo G (2014) A review on gravitational search algorithm and its applications to data clustering & classification[J]. Int J Intell Syst Appl 6(6):79

    Google Scholar 

  • Li K, Tan Z (2019) An improved flower pollination optimizer algorithm for multilevel image thresholding[J]. IEEE Access 7:165571–165582

    Article  Google Scholar 

  • Nagpal S, Arora S, Dey S (2017) Feature selection using gravitational search algorithm for biomedical data[J]. Procedia Comput Sci 115:258–265

    Article  Google Scholar 

  • Nobahari H, Nikusokhan M, Siarry P (2012) A multi-objective gravitational search algorithm based on non-dominated sorting. Int J Swarm Intell Res 3(3):32–49

    Article  Google Scholar 

  • Pare S, Bhandari AK, Kumar A et al (2018) A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm[J]. Comput Electr Eng 70:476–495

    Article  Google Scholar 

  • Raja NSM, Fernandes SL, Dey N et al (2018) Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation[J]. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0854-8

  • Rashedi E, Nezamabadi-Pour H (2013) A stochastic gravitational approach to feature based color image segmentation[J]. Eng Appl Artif Intell 26(4):1322–1332

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm[J]. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  • Rashedi E, Rashedi E, Nezamabadi-pour H (2018) A comprehensive survey on gravitational search algorithm[J]. Swarm and Evolut Comput 41:141–158

    Article  Google Scholar 

  • Sarafrazi S, Nezamabadi-pour H, Seydnejad SR (2015) A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization[J]. J King Saud Univ-Comput Inf Sci 27(3):288–296

    Google Scholar 

  • Sarkar S, Das S, Chaudhuri SS (2015) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution[J]. Pattern Recogn Lett 54:27–35

    Article  Google Scholar 

  • Sha C, Hou J, Cui H (2016) A robust 2D Otsu’s thresholding method in image segmentation[J]. J Vis Commun Image Represent 41:339–351

    Article  Google Scholar 

  • Sun G, Zhang A, Jia X et al (2016) DMMOGSA: Diversity-enhanced and memory-based multi-objective gravitational search algorithm[J]. Inf Sci 363:52–71

    Article  Google Scholar 

  • Sun G, Ma P, Ren J et al (2018) A stability constrained adaptive alpha for gravitational search algorithm[J]. Knowl-Based Syst 139:200–213

    Article  Google Scholar 

  • Xiong L, Chen R, Zhou X et al (2019a) Multi-feature fusion and selection method for an improved particle swarm optimization[J]. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01624-4

    Article  Google Scholar 

  • Xiong L, Zhang D, Li K et al (2019b) The extraction algorithm of color disease spot image based on Otsu and watershed[J]. Soft Comput. https://doi.org/10.1007/s00500-019-04339-y

    Article  Google Scholar 

  • Yazdani S, Nezamabadi-pour H, Kamyab S (2014) A gravitational search algorithm for multimodal optimization[J]. Swarm Evolut Comput 14:1–14

    Article  Google Scholar 

  • Zeng N, Wang Z, Zhang H et al (2016) Deep belief networks for quantitative analysis of a gold immunochromatographic strip[J]. Cogn Comput 8(4):684–692

    Article  Google Scholar 

  • Zeng N, Qiu H, Wang Z et al (2018) A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease[J]. Neurocomputing 320:195–202

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by GDAS' Project of Science and Technology Development (2019GDASYL- 0103077, 2018GDASCX-0115, 2017GDASCX-0115).

Author information

Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Zhiping Tan

  2. Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, 510070, China

    Dongbo Zhang

Authors
  1. Zhiping Tan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dongbo Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dongbo Zhang.

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

Tan, Z., Zhang, D. A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation. J Ambient Intell Human Comput 11, 4983–4994 (2020). https://doi.org/10.1007/s12652-020-01777-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01777-7

Keywords