Skip to main content

Quantum-inspired ant lion-optimized hybrid fuzzy c-means method for fuzzy clustering and image segmentation

Abstract

In the research field of computer vision and image recognition, images need to be preprocessed, and image segmentation is an essential method of image preprocessing. Researchers usually use fuzzy clustering algorithms for image preprocessing. The fuzzy c-means (FCM) method is easy to implement, so it is the most widely used and more successful among many fuzzy clustering algorithms. However, the algorithm is highly sensitive to randomly initialized cluster centers, and the final result is highly likely to be a local optimal value. Therefore, we use quantum-inspired ant lion optimizer (QALO) to optimize FCM clustering method and propose an efficient hybrid clustering algorithm called QALOFCM. The UCI dataset is used as an experimental dataset to testify the clustering effect of the method and compare and analyze the clustering results with other well-known clustering algorithms. The algorithm is designed to perform image segmentation experiments on Simulated Brain Database. The final comparative experiment can draw a conclusion that the hybrid method can not only be effectively used for fuzzy clustering, but also can be effectively applied in image segmentation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Data Availability

The datasets analyzed during the current study are available in the Machine Learning Repository, archive.ics.uci.edu, and Simulated Brain Database, brainweb.bic.mni.mcgill.ca.

References

  1. Bache K, Lichman M (2013) UCI Machine learning repository. University of California, Irvine, CA

    Google Scholar 

  2. Benioff P (1982) Quantum mechanical Hamiltonian models of Turing machines. J Stat Phys 29(3):515–546

    MathSciNet  MATH  Article  Google Scholar 

  3. Beyer HG, Schwefel HP (2002) Evolution strategiesCA Comprehensive Introduction. Nat Comput 1:3–52

    MathSciNet  MATH  Article  Google Scholar 

  4. Bezdek JC (1974) Cluster validity with fuzzy sets. J Cybernet 3:58–73

    MathSciNet  MATH  Article  Google Scholar 

  5. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York

    MATH  Book  Google Scholar 

  6. Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

  7. Bezdek JC (1975) Mathematical models for systematics and taxonomy. In: Proceedings of the 8th international conference on numerical. taxonomy, San Francisco, CA, pp 143–165

  8. Bui DT, Ngo PTT, Pham TD et al (2019) A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. CATENA 179:184–196

    Article  Google Scholar 

  9. Chaghari A, Feizi-Derakhshi MR, Balafar MA (2018) Fuzzy clustering based on Forest optimization algorithm. J King Saud Univ—Comput Inf Sci 30(1):25–32

  10. Chen J, Qi X, Chen L et al (2020) Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowl-Based Syst 203:106167

    Article  Google Scholar 

  11. Cocosco CA, Kollokian V, Kwan RKS et al (1997) Brainweb: Online interface to a 3D MRI simulated brain database. NeuroImage

  12. Das S, Abraham A, Konar A (2008) Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. Advances of computational intelligence in industrial systems. Springer, Berlin, Heidelberg, pp 1–38

    Google Scholar 

  13. Das P, Naskar SK, Patra SN (2018) Hardware efficient FIR filter design using global best steered quantum inspired cuckoo search algorithm. Appl Soft Comput 71:1–19

    Article  Google Scholar 

  14. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  15. Fang W, Sun J, Chen H et al (2016) A decentralized quantum-inspired particle swarm optimization algorithm with cellular structured population. Inf Sci 330:19–48

    Article  Google Scholar 

  16. Feynman RP (1982) Simulating physics with computers. Int J Theor Phys 21(6):467–488

    MathSciNet  Article  Google Scholar 

  17. Figueiredo E, Macedo M, Siqueira HV et al (2019) Swarm intelligence for clustering—a systematic review with new perspectives on data mining. Eng Appl Artif Intell 82:313–329

    Article  Google Scholar 

  18. Fu KS, Mui JK (1981) A survey on image segmentation. Pattern Recognit 13(1):3–16

    MathSciNet  Article  Google Scholar 

  19. Gamino-Sánchez F, Hernández-Gutiérrez IV, Rosales-Silva AJ et al (2018) Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise. Eng Appl Artif Intell 73:31–49

    Article  Google Scholar 

  20. Guo L, Chen L, Chen CLP et al (2018) Integrating guided filter into fuzzy clustering for noisy image segmentation. Digit Signal Prog 83:235–248

    Article  Google Scholar 

  21. Jiao X, Chen Y, Dong R (2020) An unsupervised image segmentation method combining graph clustering and high-level feature representation. Neurocomputing 409:83–92

    Article  Google Scholar 

  22. Jie L, Liu W, Sun Z et al (2017) Hybrid fuzzy clustering methods based on improved self-adaptive cellular genetic algorithm and optimal-selection-based fuzzy c-means. Neurocomputing 249:140–156

    Article  Google Scholar 

  23. 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. Appl Soft Comput 66:319–329

    Article  Google Scholar 

  24. Konar D, Bhattacharyya S, Sharma K et al (2017) An improved hybrid quantum-inspired genetic algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Appl Soft Comput 53:296–307

    Article  Google Scholar 

  25. Kong Y, Wu J, Yang G et al (2019) Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation. J Neurosci Methods 311:17–27

    Article  Google Scholar 

  26. Krinidis S, Chatzis V (2010) A robust fuzzy local information C-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337

    MathSciNet  MATH  Article  Google Scholar 

  27. Li C, Zhou J, Kou P et al (2012) A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing 83:98–109

    Article  Google Scholar 

  28. Mahajan M, Nimbhorkar P, Varadarajan K (2009) The planar k-means problem is NP-hard. International workshop on algorithms and computation. Springer, Berlin, Heidelberg, pp 274–285

    Google Scholar 

  29. Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1–17

    Article  Google Scholar 

  30. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  31. Miyamoto S, Umayahara K (2000) Methods in hard and fuzzy clustering. Soft computing and human-centered machines. Springer, Tokyo, pp 85–129

    MATH  Chapter  Google Scholar 

  32. Nayak J, Naik B, Kanungo DP et al (2018) A hybrid elicit teaching learning based optimization with fuzzy c-means (ETLBO-FCM) algorithm for data clustering. Ain Shams Eng J 9(3):379–393

    Article  Google Scholar 

  33. Pradhan K, Basu S, Thakur K et al (2020) Imprecise modified solid green traveling purchaser problem for substitute items using quantum-inspired genetic algorithm. Comput Ind Eng 147:106578

    Article  Google Scholar 

  34. Raja JB, Pandian SC (2020) PSO-FCM based data mining model to predict diabetic disease. Comput Meth Programs Biomed 196:105659

    Article  Google Scholar 

  35. Shang R, Chen C, Wang G et al (2020) A thumbnail-based hierarchical fuzzy clustering algorithm for SAR image segmentation. Signal Process 171:107518

    Article  Google Scholar 

  36. Sheykhizadeh S, Naseri A (2018) An efficient swarm intelligence approach to feature selection based on invasive weed optimization: application to multivariate calibration and classification using spectroscopic data. Spectroc A-Molec Biomolec Spectr 194:202–210

    Article  Google Scholar 

  37. Silva Filho TM, Pimentel BA, Souza RMCR et al (2015) Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst Appl 42(17–18):6315–6328

    Article  Google Scholar 

  38. Silva-Santos CH, Morais JVF, Bertelli F et al (2020) Purification of naphthalene by zone refining: mathematical modelling and optimization by swarm intelligence-based techniques. Sep Purif Technol 234:116089

    Article  Google Scholar 

  39. Singh P (2020) A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: A special application in segmentation of Parkinson’s disease (PD) MR images. Comput Meth Programs Biomed 189:105317

    Article  Google Scholar 

  40. Srikanth K, Panwar LK, Panigrahi BK et al (2018) Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem. Comput Electr Eng 70:243–260

    Article  Google Scholar 

  41. Taherdangkoo M, Bagheri MH (2013) A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms. Eng Appl Artif Intell 26(5–6):1493–1502

    Article  Google Scholar 

  42. Xie XL, Beni A (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3:841–846

    Article  Google Scholar 

  43. Yang Y, Wang R, Feng C (2020) Level set formulation for automatic medical image segmentation based on fuzzy clustering. Signal Process-Image Commun 87:115907

    Article  Google Scholar 

  44. Yu C, Cai Z, Ye X et al (2020) Quantum-like mutation-induced dragonfly-inspired optimization approach. Math Comput Simul 178:259–289

    MathSciNet  MATH  Article  Google Scholar 

  45. Yuan X, Wang P, Yuan Y et al (2015) A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem. Energy Conv Manag 100:1–9

    Article  Google Scholar 

  46. Zhu H, Qi X, Chen F et al (2019) Quantum-inspired cuckoo co-search algorithm for no-wait flow shop scheduling. Appl Intell 49(2):791–803

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61972438) and Key Research and Development Projects in Anhui Province (202004a05020002).

Funding

This study was funded by the National Natural Science Foundation of China (61972438) and Key Research and Development Projects in Anhui Province (202004a05020002).

Author information

Affiliations

Authors

Contributions

All authors contributed to the study conception and design. J. Chen and X. Qi conceived the presented idea. J. Chen carried out the experiment and wrote the manuscript with support from X. Qi, F. Chen, and G. Cheng. F. Chen and G. Cheng helped in supervising the findings of this work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fulong Chen.

Ethics declarations

Conflict of interest

We declare that this research does not have any conflicts of interest.

Ethical approval

This chapter does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Additional informed consent was obtained from all individual participants for whom identifying information is included in this chapter.

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

Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Qi, X., Chen, F. et al. Quantum-inspired ant lion-optimized hybrid fuzzy c-means method for fuzzy clustering and image segmentation. Soft Comput 25, 15021–15034 (2021). https://doi.org/10.1007/s00500-021-06391-z

Download citation

Keywords

  • Image segmentation
  • Fuzzy clustering
  • Fuzzy c-means
  • Quantum-inspired ant lion optimizer