Skip to main content
SpringerLink
Log in

Entropy-based circular histogram thresholding for color image segmentation

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Circular histogram thresholding on hue component is an important method in color image segmentation. However, existing circular histogram thresholding method based on Otsu criterion lacks the universality. To reduce the complexity and enhance the universality of thresholding on circular histogram, the cumulative distribution function is firstly introduced into circular histogram. Then, this paper expands circular histogram into the linearized one in anticlockwise direction or clockwise one by using optimal entropy of cumulative distribution function. In the end, fuzzy entropy thresholding method is utilized on linearized histogram to select optimal threshold for color image segmentation. Experimental results indicate that the proposed method has better performance and adaptability than the existing circular histogram thresholding method, which can increase pixel accuracy index by 30.12% and structure similarity index by 27.53%, respectively.

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
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Sharma, N., Mishra, M., Shrivastava, M.: Color image segmentation techniques and issues: an approach. Int. J. Sci. Technol. Res. 1(41), 2277–8616 (2012)

    Google Scholar 

  2. Garcia-Lamont, F., Cervantes, J., López, A., Rodriguez, L.: Segmentation of images by color features: a survey. Neurocomputing 292(1), 1–27 (2018)

    Article  Google Scholar 

  3. Gothwal, R., Gupta, S., Gupta, D., Dahiya, A.K.: Color image segmentation algorithm based on RGB channels. In: The 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), Oct, Noida, India. IEEE (2014)

  4. Kumar, A., Thakur, V., Ranout, P.: Improved color image segmentation based on RGB and HSI. Int. J. Eng. Dev. Res. (IJEDR) 3(2), 969–988 (2015)

    Google Scholar 

  5. Gargi, S., Kishor, B.: Color image segmentation in HSI color space based on color JND histogram. Int. J. Image Process. Vis. Commun 2(20–27), 2319-1724 (2014)

    Google Scholar 

  6. Li, Z.Y., Yu, Z.C., Liu, W.X., Zhang Z.C.: Tongue image segmentation via color decomposition and thresholding. In: The 4th International Conference on Information Science and Control Engineering, Jul, Changsha, China. IEEE (2017)

  7. Markchom, T., Lipikorn, R.: Thin cloud removal using local minimization and　logarithm image transformation in HSI color space. In: The 4th International Conference on Front Signal Process, Sept, Poitiers, France. IEEE (2018)

  8. Tseng, D.C., Li, Y.F., Tung, C.T.: Circular histogram thresholding for color image segmentation. In: The 3rd International Conference on Document Anal Recognit, Aug, Montreal, Quebec, Canada. IEEE (1995)

  9. Wu, J., Zeng, P., Zhou, Y., Olivier, C.: A novel color image segmentation method and its application to white blood cell image analysis. In: the 3rd International Conference on Signal Process, Nov, Beijing, China. IEEE (2006)

  10. Dimov, D., Laskov, L.: Circular histogram thresholding and multithresholding. In: The 3rd International Conference on Computer Systems Technology Workshop for PhD Students in Comput, Jun, Rousse, Bulgaria (2009)

  11. Lai, Y.K., Rosin, P.L.: Efficient circular thresholding. IEEE Trans. Image Process. 23(3), 992–1001 (2014)

    Article  MathSciNet  Google Scholar 

  12. Otsu, N.: A thresholding selection method from gray-level histograms. IEEE Trans. Syst. 9(1), 62–66 (1979)

    Google Scholar 

  13. Fujita, K.: A clustering method for data in cylindrical coordinates. Math. Probl. Eng. 1(11) (2017)

  14. Kurita, T., Otsu, N., Abdelmalek, N.N.: Maximum likelihood thresholding based on population mixture models. Pattern Recognit. 25(10), 1231–1240 (1992)

    Article  Google Scholar 

  15. Xu, X., Xu, S., Jin, L.: Characteristic analysis of Otsu threshold and its applications. Pattern Recognit. Lett. 32(7), 956–961 (2011)

    Article  Google Scholar 

  16. Kittler, J., Illingwotth, J.: On threshold selection using clustering criteria. IEEE Trans. Syst. Man Cybern. 15(5), 652–655 (1985)

    Article  Google Scholar 

  17. Kapur, J., Sahoo, P., Wong, A.: A new method for gray-level picture thresholding using the entropy of the histogram. Signal. Process. 2(3), 273–285 (1980)

    Google Scholar 

  18. Zhao, L., Kan, L.: A validation metric for model with mixture of random and interval variables. J. Beijing Univ. Aeronaut. Astronaut. 44(5), 967–974 (2018)

    Google Scholar 

  19. Pernot, P., Savin, A.: Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors. J. Chem. Phys. 148(24) (2018)

  20. Clark, T.D., Larson, J.M., Mordeson, J.N.: Fuzzy set theory-from applying fuzzy mathematics to formal models in comparative politics. In: Studies in Fuzziness and Soft Computing, 225, pp. 29–63. Springer Press, Berlin (2016)

  21. Xian, S.D., Jing, N.: A Novel Approach Based on Intuitionistic Fuzzy Combined Ordered Weighted Averaging Operator for Group Decision Making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26(3), 493–518 (2018)

    Article  MathSciNet  Google Scholar 

  22. Ananthi, V.P., Balasubramaniam, P.: A new thresholding technique based on fuzzy set as an application to leukocyte nucleus segmentation. Comput. Methods Programs Biomed. 134, 165–177 (2016)

    Article  Google Scholar 

  23. Naidu, M.S.R., Kumar, R., Chiranjeevi, K.: Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Eng. J. 57(3), 1643–1655 (2017)

    Article  Google Scholar 

  24. Liu, S.K., Xu, Z.H., Gao, J.: A fuzzy compromise programming model based on the modified S-curve membership functions for supplier selection. Granular Comput 3, 275–283 (2018)

    Article  Google Scholar 

  25. Pan, J.J., Zheng, X.W., Sun, L., Yang, L.N., Wang, Y.L.: Image segmentation based on 2D OTSU and simplified swarm optimization. In: 2016 International Journal of Machine Learning and Cybernetics (ICMLC), Jul, pp. 1026–1030, Jeju, South Korea. IEEE (2016)

  26. Ishak, B.: A two-dimensional multilevel thresholding method for image segmentation. Appl. Soft Comput. 52(11), 306–322 (2017)

    Article  Google Scholar 

  27. Benoit, H.: Advances in multimedia information processing–PCM 2013. Lect. Notes Comput. Sci. 5879(7499), 201–204 (2013)

    Google Scholar 

  28. Li, H., Suen, C.Y.: A novel Non-local means image denoising method based on grey theory. Pattern Recognit. 49(1), 237–248 (2016)

    Article  Google Scholar 

  29. Sheet, D., Garud, H., Suveer, A.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consumer Electron. 56(4), 2475–2480 (2010)

    Article  Google Scholar 

  30. Ananthi, V.P., Balasubramaniam, P., Raveendran, P.: A thresholding method based on interval-valued intuitionistic fuzzy sets: an application to image segmentation. Pattern Anal. Appl. 21(4), 1039 (2018)

    Article  MathSciNet  Google Scholar 

  31. Berezsky, O.M., Pitsun, O.Y.: Evaluation methods of image segmentation quality. Radio Electron. Comput. Sci. Control 119–128 (2018)

  32. Zhang, X.L., Feng, X.Z., Xiao, P.F., He, G.J., Zhu, L.J.: Segmentation quality evaluation using region-based precision and recall measures for remote sensing images. ISPRS J. Photogramm. 102, 73–84 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Chao Kang & Jiulun Fan

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

    Chengmao Wu

Authors
  1. Chao Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengmao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiulun Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Kang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by National Natural Science Foundation of China, Grant Number 61671377.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, C., Wu, C. & Fan, J. Entropy-based circular histogram thresholding for color image segmentation. SIViP 15, 129–138 (2021). https://doi.org/10.1007/s11760-020-01723-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01723-2

Keywords

Search

Navigation