Skip to main content
SpringerLink
Log in

Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise.

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

Access this article

Log in via an institution

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

The MR brain images generated and analyzed during this study are available in the Kaggle brain tumor dataset: https://www.kaggle.com/hasimdev/brain-mri-dataset. The remote sensing images generated and analyzed during this study are available in the UC Merced Land Use dataset: http://vision.ucmerced.edu/datasets/landuse.html. The natural images generated and analyzed during this study are available in the Berkeley Segmentation Dataset: https://doi.org/10.1109/ICCV.2001.937655

References

  1. Shivhare, P., Gupta, V.: Review of image segmentation techniques including Pre & Post processing operations. Int. J. Eng. Adv. Technol. 4(3), 153–157 (2015)

    Google Scholar 

  2. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000). https://doi.org/10.1109/34.868688

    Article  Google Scholar 

  3. Tang, Y.C., Chen, M.Y., Wang, C.L., et al.: Recognition and localization methods for vision-based fruit picking robots: a review. Front Plant Sci. 11, 1–17 (2020). https://doi.org/10.3389/fpls.2020.00510

    Article  Google Scholar 

  4. Tang, Y.C., Lin, L.J., Wang, C.L., et al.: Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision. Robot. Comput.-Integr. Manuf. 59, 36–46 (2019). https://doi.org/10.1016/j.rcim.2019.03.001

    Article  Google Scholar 

  5. Fuchs, P., Kröger, T., Garbe, C.S.: Defect detection in CT scans of cast aluminum parts: a machine vision perspective. Neurocomputing 453, 85–96 (2021). https://doi.org/10.1016/j.neucom.2021.04.094

    Article  Google Scholar 

  6. Isidoro, J.M.G.P., Martins, R., Carvalho, R.F., et al.: A high-frequency low-cost technique for measuring small-scale water level fluctuations using computer vision. Measurement 180, 109477 (2021). https://doi.org/10.1016/j.measurement.2021.109477

    Article  Google Scholar 

  7. Chen, M.Y., Tang, Y.C., Zhou, X.J., et al.: 3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM. Comput. Electron. Agric. 187, 106237 (2021). https://doi.org/10.1016/j.compag.2021.106237

    Article  Google Scholar 

  8. Zhang, Q.Z., Qin, Z.: Application of machine vision technology in road detection. Civ. Eng. J. 27(4), 513–524 (2018). https://doi.org/10.14311/CEJ.2018.04.0041

    Article  Google Scholar 

  9. Carata, S.V., Neagoe, V.E.: A pulse-coupled neural network approach for image segmentation and its pattern recognition application. In: International Conference on Communications (COMM), IEEE, (2016). https://doi.org/10.1109/ICComm.2016.7528317

  10. Luo, J., Wang, Y., Wang, Q., et al.: Automatic image segmentation of grape based on computer vision. Recent Dev. Intell. Syst. Interact. Appl. 541, 365–370 (2016). https://doi.org/10.1007/978-3-319-49568-2_52

    Article  Google Scholar 

  11. Kumar, S.V.A., Harish, B.S., Aradhya, V.N.M.: A picture fuzzy clustering approach for brain tumor segmentation. In: Proc of 2016 Second International Conference on Cognitive Computing and Information Processing, Mysore: IEEE, pp. 1–6 (2016). https://doi.org/10.1109/CCIP.2016.7802852

  12. Alipour, S., Shanbehzadeh, J.: Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method. Mach. Vis. Appl. 25(6), 1469–1488 (2014). https://doi.org/10.1007/s00138-014-0606-5

    Article  Google Scholar 

  13. Li, C., Huang, R., Ding, Z., et al.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011). https://doi.org/10.1109/tip.2011.2146190

    Article  MathSciNet  MATH  Google Scholar 

  14. Wang, L., Chen, Y., Pan, X., et al.: Level set segmentation of brain magnetic resonance images based on local gaussian distribution fitting energy. J. Neurosci. Methods 188(2), 316–325 (2010). https://doi.org/10.1016/j.jneumeth.2010.03.004

    Article  Google Scholar 

  15. Chen, X., Nguyen, B.P., Chui, C.K., et al.: Automated brain tumor segmentation using kernel dictionary learning and super pixel-level features. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 002,547–002,552 (2016). https://doi.org/10.1109/SMC.2016.7844622

  16. Balafar, M.A., Ramli, A.R., Saripan, M.I., et al.: Review of brain MRI image segmentation methods. Artif. Intell. Rev. 33(3), 261–274 (2010). https://doi.org/10.1007/s10462-010-9155-0

    Article  Google Scholar 

  17. Benaichouche, A.N., Oulhadj, H., Siarry, P.: Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digit. Signal Process. 23(5), 1390–1400 (2013). https://doi.org/10.1016/j.dsp.2013.07.005

    Article  MathSciNet  Google Scholar 

  18. Huang, C.W., Lin, K.P., Wu, M.C., et al.: Intuitionistic fuzzy c-means clustering algorithm with neighborhood attraction in segmenting medical image. Soft Comput. 19(2), 459–470 (2015). https://doi.org/10.1007/s00500-014-1264-2

    Article  Google Scholar 

  19. Krinidis, S., Chatzis, V.: A robust fuzzy local information c-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010). https://doi.org/10.1109/TIP.2010.2040763

    Article  MathSciNet  MATH  Google Scholar 

  20. Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: a survey. Med. Image Anal. 5(2), 127–142 (2001). https://doi.org/10.1016/S1361-8415(00)00041-4

    Article  Google Scholar 

  21. Qiu, C., Xiao, J., Yu, L., et al.: A modified interval type-2 fuzzy c-means algorithm with application in MR image segmentation. Pattern Recognit. Lett. 34(12), 1329–1338 (2013). https://doi.org/10.1016/j.patrec.2013.04.021

    Article  Google Scholar 

  22. Coleman, G.B., Andrews, H.C.: Image segmentation by clustering. P. IEEE 67(5), 773–785 (1979). https://doi.org/10.1109/PROC.1979.11327

    Article  Google Scholar 

  23. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981). https://doi.org/10.1007/978-1-4757-0450-1〹

    Book  MATH  Google Scholar 

  24. Ahmed, M.N., Yamany, S.M., Mohamed, N., et al.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21(3), 193–199 (2002). https://doi.org/10.1109/42.996338

    Article  Google Scholar 

  25. Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. 34(4), 1907–1916 (2004). https://doi.org/10.1109/TSMCB.2004.831165

    Article  Google Scholar 

  26. Szilagyi, L., Benyo, Z., Szilagyi, S.M., et al.: MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 724–726 (2003). https://doi.org/10.1109/IEMBS.2003.1279866

  27. Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit. 40(3), 825–838 (2007). https://doi.org/10.1016/j.patcog.2006.07.011

    Article  MATH  Google Scholar 

  28. Pelekis, N., Iakovidis, D.K., Kotsifakos, E.E., et al.: Fuzzy clustering of intuitionistic fuzzy data. Int. J. Bus. Intell. Data Min. 3(1), 45–65 (2008). https://doi.org/10.1504/IJBIDM.2008.017975

    Article  Google Scholar 

  29. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986). https://doi.org/10.1016/S0165-0114(86)80034-3

    Article  MATH  Google Scholar 

  30. Chaira, T.: A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images. Appl. Soft Comput. 11(2), 1711–1717 (2011). https://doi.org/10.1016/j.asoc.2010.05.005

    Article  Google Scholar 

  31. Yager, R.R.: On the measure of fuzziness and negation Part I: membership in the unit interval. Int. J. Gen. Syst. 5(4), 221–229 (1979). https://doi.org/10.1080/03081077908547452

    Article  MATH  Google Scholar 

  32. Yager, R.R.: On the measure of fuzziness and negation II. Lattices. Inf. Technol. Control 44(3), 236–260 (1980). https://doi.org/10.1016/S0019-9958(80)90156-4

    Article  MathSciNet  MATH  Google Scholar 

  33. Chaira, T., Panwar, A.: An Atanassov’s intuitionistic fuzzy kernel clustering for medical image segmentation. Int. J. Comput. Int. Sys. 7(2), 360–370 (2014). https://doi.org/10.1080/18756891.2013.865830

    Article  Google Scholar 

  34. Xu, Z., Wu, J.: Intuitionistic fuzzy c-means clustering algorithms J. . Syst. Eng. Electron. 21(4), 580–590 (2010). https://doi.org/10.3969/j.issn.1004-4132.2010.04.009

    Article  Google Scholar 

  35. Verma, H., Agrawal, R.K.: Possibilistic intuitionistic fuzzy c-means clustering algorithm for MRI brain image segmentation. Int. J. Artif. Intell. Tools 24(5), 1550016 (1–24) (2015). https://doi.org/10.1142/S0218213015500165

    Article  Google Scholar 

  36. Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993). https://doi.org/10.1109/91.227387

    Article  Google Scholar 

  37. Kaur, P.: Intuitionistic fuzzy sets based credibilistic fuzzy C-means clustering for medical image segmentation. Int. J. Inf. Technol. 9(4), 345–351 (2017). https://doi.org/10.1007/s41870-017-0039-2

    Article  Google Scholar 

  38. Kumar, D., Verma, H., Mehra, A., et al.: A modified intuitionistic fuzzy c-means clustering approach to segment human brain MRI image. Multimed. Tools Appl. 78(6), 1–25 (2018). https://doi.org/10.1007/s11042-018-5954-0

    Article  Google Scholar 

  39. Arora, J., Tushir, M.: A new semi-supervised intuitionistic fuzzy c-means clustering. ICST Trans. Scalable Inf. Syst. 7(24), 159622 (2019). https://doi.org/10.4108/eai.13-7-2018.159622

    Article  Google Scholar 

  40. Dante, M.V.: Redescending intuitionistic fuzzy clustering to brain magnetic resonance image segmentation. J. Intell. Fuzzy Syst. 39(1), 1097–1108 (2020). https://doi.org/10.3233/JIFS-192005

    Article  Google Scholar 

  41. Kumar, D., Agrawal, R.K., Verma, H.: Kernel intuitionistic fuzzy entropy clustering for MRI image segmentation. Soft Comput. 24(6), 4003–4026 (2020). https://doi.org/10.1007/s00500-019-04169-y

    Article  Google Scholar 

  42. Zhao, F., Zeng, Z., Liu, H., et al.: Semi-supervised approach to surrogate-assisted multi objective kernel intuitionistic fuzzy clustering algorithm for color image segmentation. IEEE Trans. Fuzzy Syst. 28(6), 1023–1034 (2020). https://doi.org/10.1109/TFUZZ.2020.2973121

    Article  Google Scholar 

  43. Kaur, P., Soni, A.K., Gosain, A.: Novel intuitionistic fuzzy c-means clustering for linearly and nonlinearly separable data. WSEAS Trans. Comput. 11(3), 65–76 (2012)

    Google Scholar 

  44. Tsai, D.M., Lin, C.C.: Fuzzy c-means based clustering for linearly and nonlinearly separable data. Pattern Recognit. 44, 1750–1760 (2011). https://doi.org/10.1016/j.patcog.2011.02.009

    Article  MATH  Google Scholar 

  45. Wang, Z., Fan, J., Lou, H., et al.: Intuitionistic fuzzy c-mean clustering algorithm incorporating local information for image segmentation. Appl. Res. Comput. 31(9), 2864–2872 (2014). https://doi.org/10.3969/j.issn.1001-3695.2014.09.073(inChinese)

    Article  Google Scholar 

  46. Lin, K.P.: A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 22(5), 1074–1087 (2014). https://doi.org/10.1109/TFUZZ.2013.2280141

    Article  Google Scholar 

  47. Verma, H., Agrawal, R.K., Sharan, A.: An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Appl. Soft Comput. 46(C), 543–557 (2015). https://doi.org/10.1016/j.asoc.2015.12.022

    Article  Google Scholar 

  48. Chen, X., Li, D., Wang, X., et al.: Rough intuitionistic type-2 fuzzy c-means clustering algorithm for MR image segmentation. IET Image Process. 13(4), 607–614 (2019). https://doi.org/10.1049/iet-ipr.2018.5597

    Article  Google Scholar 

  49. Arora, J., Tushir, M.: Robust spatial intuitionistic fuzzy c-means with city-block distance clustering for image segmentation. J. Intell. Fuzzy Syst. 35(5), 5255–5264 (2018). https://doi.org/10.3233/JIFS-169809

    Article  Google Scholar 

  50. Zhao, F., Fan, J., Liu, H., et al.: Noise robust multi-objective evolutionary clustering image segmentation motivated by intuitionistic fuzzy information. IEEE T. Fuzzy Syst. 27(2), 387–401 (2018). https://doi.org/10.1109/TFUZZ.2018.2852289

    Article  Google Scholar 

  51. Zhao, F., Liu, H., Fan, J., et al.: Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation. Neurocomputing 312, 296–309 (2018). https://doi.org/10.1016/j.neucom.2018.05.116

    Article  Google Scholar 

  52. Bai, X., Zhang, Y., Liu, H., et al.: Intuitionistic center-free FCM clustering for MR brain image segmentation. IEEE J. Biomed. Health Inform. 23(5), 2039–2051 (2019). https://doi.org/10.1109/JBHI.2018.2884208

    Article  Google Scholar 

  53. Dante, M.V., Kinani, J.M.V., Rubio, J.D.J.: Color-based image segmentation by means of a robust intuitionistic fuzzy c-means algorithm. Int. J. Fuzzy Syst. 22(3), 901–916 (2020). https://doi.org/10.1007/s40815-020-00824-x

    Article  Google Scholar 

  54. Liu, M.: Total Bregman Divergence, a Robust Divergence Measure, and Its Applications. University of Florida (2011)

    Google Scholar 

  55. Liu, M., Vemuri, B.C., Amari, S., et al.: Shape retrieval using hierarchical total Bregman soft clustering. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2407–2419 (2012). https://doi.org/10.1109/tpami.2012.44

    Article  Google Scholar 

  56. Vemuri, B.C., Liu, M., Amari, S.I., et al.: Total Bregman divergence and its applications to DTI analysis. IEEE Trans. Med. Imaging 30(2), 475–483 (2011). https://doi.org/10.1109/TMI.2010.2086464

    Article  Google Scholar 

  57. Wu, C., Sun, J.: Adaptive robust graph fuzzy clustering algorithm based on total divergence. Acta Armamentarii 40(09), 1890–1901 (2019). https://doi.org/10.3969/j.issn.1000-1093.2019.09.014(inChinese)

    Article  Google Scholar 

  58. Hua, X., Cheng, Y., Wang, H., et al.: Geometric target detection based on total Bregman divergence. Digit. Signal Process. 75(75), 232–241 (2018). https://doi.org/10.1016/j.dsp.2018.01.008

    Article  MathSciNet  Google Scholar 

  59. Hua, X., Fan, H., Cheng, Y., et al.: Information geometry for radar target detection with total Jensen-Bregman divergence. Entropy 20(4), 256 (2018). https://doi.org/10.3390/e20040256

    Article  Google Scholar 

  60. Despotović, I., Ansteenkiste, E., Philips, W.: Spatially coherent fuzzy clustering for accurate and noise-robust image segmentation. IEEE Signal Process. Lett. 20(4), 295–298 (2013). https://doi.org/10.1109/LSP.2013.2244080

    Article  Google Scholar 

  61. Wu, C., Yang, X.: Robust credibilistic fuzzy local information clustering with spatial information constraints. Digit. Signal Process. 97, 102615 (2020). https://doi.org/10.1016/j.dsp.2019.102615

    Article  Google Scholar 

  62. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  63. Vlachos, I.K., Sergiadis, G.D.: Towards intuitionistic fuzzy image processing. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC) 1, pp. 2–7 (2005). https://doi.org/10.1109/CIMCA.2005.1631233

  64. Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst. 114(3), 505–518 (2000). https://doi.org/10.1016/S0165-0114(98)00244-9

    Article  MathSciNet  MATH  Google Scholar 

  65. Banerjee, A., Merugu, S., Dhillon, I.S., et al.: Clustering with Bregman divergences. J. Mach. Learn. Res. 6(4), 1705–1749 (2005). https://doi.org/10.1137/1.9781611972740.22

    Article  MathSciNet  MATH  Google Scholar 

  66. Hasnat, M.A., Alata, O., Tremeau, A.: Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis. Stats Comput. 26(4), 861–880 (2016). https://doi.org/10.1007/s11222-015-9576-3

    Article  MathSciNet  MATH  Google Scholar 

  67. Bregman, L.M.: The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming. USSR Comput. Math. Math. Phys. 7(3), 200–217 (1967). https://doi.org/10.1016/0041-5553(67)90040-7

    Article  MathSciNet  Google Scholar 

  68. Groen, P.D.: An introduction to total least squares. Nieuw Archief voor Wiskunde 14(4), 237–253 (1996)

    MathSciNet  MATH  Google Scholar 

  69. Wu, C., Zhang, X.: Total Bregman divergence-based fuzzy local information c-means clustering for robust image segmentation. Appl. Soft Comput. 94, 106468 (2020). https://doi.org/10.1016/j.asoc.2020.106468

    Article  Google Scholar 

  70. Gong, M., Liang, Y., Shi, J., et al.: Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process. 22(2), 573–584 (2013). https://doi.org/10.1109/TIP.2012.2219547

    Article  MathSciNet  MATH  Google Scholar 

  71. Zangwill, W.I.: Nonlinear Programming: A Unified Approach. Prentice-Hall, Englewood Cliffs (1969)

    MATH  Google Scholar 

  72. Bezdek, J.C.: A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 2(1), 1–8 (1980). https://doi.org/10.1109/tpami.1980.4766964

    Article  MATH  Google Scholar 

  73. Giordana, N., Pieczynski, W.: Estimation of generalized multi-sensor hidden Markov chains and unsupervised image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 475 (1997)

    Article  Google Scholar 

  74. Guo, Y., Sengur, A.: A novel color image segmentation approach based on Neutrosophic set and modified fuzzy c-means. Circuits Syst. Signal Process. 32(4), 1699–1723 (2010). https://doi.org/10.1007/s00034-012-9531-x

    Article  MathSciNet  Google Scholar 

  75. Gharib, R.R.: Incorporating local data and KL membership divergence into hard c-means clustering for fuzzy and noise-robust data segmentation. Mach. Learn. Data Min. (2018). https://doi.org/10.5772/intechopen.74514

    Article  Google Scholar 

  76. Martin, D., Fowlkes, C., Tal, D., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp. 416–423 (2001). https://doi.org/10.1109/ICCV.2001.937655

  77. Bezdek, J.C.: Cluster validity with fuzzy sets. J. Cybern. 3(3), 58–73 (1974). https://doi.org/10.1080/01969727308546047

    Article  MathSciNet  MATH  Google Scholar 

  78. Kumar, D., et al.: A modified intuitionistic fuzzy c-means clustering approach to segment human brain MRI image. Multimed. Tools and Appl. 78(6), 12663–12687 (2019). https://doi.org/10.1007/s11042-018-5954-0

    Article  Google Scholar 

  79. Zhao, F., Fan, J.L., Liu, H.Q.: Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non-local spatial information for image segmentation. Expert Syst. Appl. 41(9), 4083–4093 (2014). https://doi.org/10.1016/j.eswa.2014.01.003

    Article  Google Scholar 

  80. Bharill, N., Tiwari, A., Malviya, A.: Fuzzy based scalable clustering algorithms for handling big data using apache spark. In 2016 IEEE Second International Conference on Big Data Computing Service and Applications (Big Data Service). IEEE 2016, pp. 95–104 (2016). https://doi.org/10.1109/TBDATA.2016.2622288

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61671377, 51709228) and the Shaanxi Natural Science Foundation of China (2016JM8034, 2017JM6107, 2018JM4018). Besides, the authors would like to thank the School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an, China, for financial support.

Author information

Authors and Affiliations

  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, People’s Republic of China

    Chengmao Wu, Congcong Huang & Jiajia Zhang

Authors
  1. Chengmao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Congcong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiajia Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Congcong Huang.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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

Wu, C., Huang, C. & Zhang, J. Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation. Vis Comput 39, 149–181 (2023). https://doi.org/10.1007/s00371-021-02319-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02319-8

Keywords

Search

Navigation