iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S10044-018-00769-W
An image thresholding approach based on Gaussian mixture model | Pattern Analysis and Applications Skip to main content
Springer Nature Link
Log in

An image thresholding approach based on Gaussian mixture model

  • Theoretical advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Image thresholding is an important technique for partitioning the image into foreground and background in image processing and analysis. It is difficult for traditional thresholding methods to get satisfactory performance on the noisy and uneven grayscale images. In this paper, we propose an image thresholding approach based on Gaussian mixture model (GMM) to solve this problem. GMM assumes that image is a mixture of two unknown parameters’ Gaussian distributions, which corresponds to foreground and background, respectively. Based on this assumption, we adopt expectation maximization algorithm with a simple initialization strategy to estimate the statistical parameters and utilize Bayesian criteria to generate the binary map. Furthermore, we calculate the posterior probabilities in consideration of neighborhood effect to achieve good performance on noisy and uneven grayscale images. Experimental results conducted on the synthetic and real images demonstrate the effectiveness of the proposed method.

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

References

  1. Nie F, Zhang P, Li J et al (2017) A novel generalized entropy and its application in image thresholding. Signal Process 134:23–34

    Article  Google Scholar 

  2. Sujaritha M, Annadurai S (2011) A new modified Gaussian mixture model for color-texture segmentation. J Comput Sci 7(2):279–283

    Article  Google Scholar 

  3. Guo Y, Şengür A, Ye J (2014) A novel image thresholding algorithm based on neutrosophic similarity score. Measurement 58:175–186

    Article  Google Scholar 

  4. Ueno K, Yamaguchi J (2012) Image thresholding by improved convergent floating thresholding method using extended control value. IEEJ Trans Electron Inf Syst 132:759–765

    Google Scholar 

  5. Li Z, Liu C, Liu G et al (2010) A novel statistical image thresholding method. AEU Int J Electron Commun 64(2):1137–1147

    Article  Google Scholar 

  6. Zou Y, Dong F, Lei B et al (2014) Maximum similarity thresholding. Digit Signal Process 28(1):120–135

    Article  Google Scholar 

  7. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–166

    Article  Google Scholar 

  8. Chakrawarty P, Bhatnagar G (2016) Image thresholding based on local activity feature matrix. Opt Int J Light Electron Opt 127(20):9037–9045

    Article  Google Scholar 

  9. Ahmadi E, Azimifar Z, Shams M et al (2015) Document image binarization using a discriminative structural classifier. Pattern Recogn Lett 63:36–42

    Article  Google Scholar 

  10. Makridis M, Papamarkos N (2010) An adaptive layer-based local binarization technique for degraded documents. Int J Pattern Recog Artif Intell 24(2):245–279

    Article  Google Scholar 

  11. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):377–393

    Article  Google Scholar 

  12. Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recogn 19(1):41–47

    Article  Google Scholar 

  13. Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recogn 33(2):225–236

    Article  Google Scholar 

  14. Moghaddam RF, Cheriet M (2010) A multi-scale framework for adaptive binarization of degraded document images. Pattern Recogn 43(6):2186–2198

    Article  MATH  Google Scholar 

  15. Bernsen J (1986) Dynamic thresholding of gray-level images. In: International conference on pattern recognition, pp 251–255

  16. Gatos B, Pratikakis I, Perantonis SJ (2006) Adaptive degraded document image binarization. Pattern Recogn 39(3):317–327

    Article  MATH  Google Scholar 

  17. Ben Ishak A (2016) A two-dimensional multilevel thresholding method for image segmentation. Appl Soft Comput 52:306–322

    Google Scholar 

  18. Bazi Y, Bruzzone L, Melgani F (2007) Image thresholding based on the EM algorithm and the generalized Gaussian distribution. Pattern Recognit 40(2):619–634

    Article  MATH  Google Scholar 

  19. Huang ZK, Chau KW (2008) A new image thresholding method based on Gaussian mixture model. Appl Math Comput 205(2):899–907

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  21. Jiang Y, Yang Z, Hao Z, Wang Y (2014) A cooperative honey bee mating algorithm and its application in multi-threshold image segmentation. In: IEEE congress on evolutionary computation, pp 1579–1585

  22. Kotte S, Kumar PR, Injeti SK (2018) An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm. Ain Shams Eng J 9(4):1043–1067

    Article  Google Scholar 

  23. Cuevas E, Zaldivar D, Pérez-Cisneros M (2011) Seeking multi-thresholds for image segmentation with Learning Automata. Mach Vis Appl 22(5):805–818

    Article  Google Scholar 

  24. Singh A, Baraskar T (2016) A survey on multi-level thresholding for image segmentation using nature inspired algorithms. In: International conference on electrical, electronics, engineering trends, communication, optimization and sciences, pp 878–883

  25. Tobias OJ, Seara R (2002) Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process 11(12):1457–1465

    Article  Google Scholar 

  26. Han J, Yang C, Zhou X, Gui W (2017) A new multi-threshold image segmentation approach using state transition algorithm. Appl Math Model 44:588–601

    Article  MathSciNet  Google Scholar 

  27. Gupta L, Sortrakul T (1998) A Gaussian-mixture-based image segmentation algorithm. Pattern Recogn 31(3):315–325

    Article  Google Scholar 

  28. Hui T, Dillenseger JL, Xu DB, Li ML (2009) A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model. Comput Med Imaging Graph 33(8):644–650

    Article  Google Scholar 

  29. Zhu F, Luo L, Song Y et al (2011) Adaptive spatially neighborhood information Gaussian mixture model for image segmentation. J Comput Res Dev 48(11):2000–2007

    Google Scholar 

  30. Li SZ, Jain AK (2015) Encyclopedia of biometrics, 2nd edn. Springer, New York

    Book  Google Scholar 

  31. Xiong TS, Huang YY (2015) Robust Gaussian mixture modelling based on spatially constraints for image segmentation. J Inf Hiding Multimed Signal Process 6(5):857–868

    Google Scholar 

  32. Dempster AP (1977) Maximum likelihood estimation from incomplete data via the EM algorithm. J R Stat Soc 39(1):1–38

    MATH  Google Scholar 

  33. Mclachlan GJ, Peel D (2004) Finite mixture models. Springer, New York

    MATH  Google Scholar 

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

    Article  Google Scholar 

  35. Sezgin M, Sankur B (2001) Selection of thresholding methods for nondestructive testing applications. In: International conference on image processing, vol 3, pp 764–767

  36. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  37. Niblack W (1986) An introduction to digital image processing. Prentice Hall, Englewood Cliffs

    Google Scholar 

  38. Gong J, Li L, Chen W (1996) A fast recursive algorithm for two-dimensional thresholding. In: International conference on signal processing, vol 2, pp 1155–1158

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41671452, 41701532), High-level talent Fund Project of Henan University of Technology (Grant No. 2018BS054) and the basic research funding of the Central University (Grant No. 2042016kf0012).

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, China

    Like Zhao

  2. School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Hongshan District, Wuhan City, 430079, Hubei Province, China

    Like Zhao, Shunyi Zheng, Wenjing Yang, Haitao Wei & Xia Huang

  3. Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Hongshan District, Wuhan City, 430079, Hubei Province, China

    Shunyi Zheng & Xia Huang

  4. Institute of Smart City, Zhengzhou University, 75 North University Road, Zhengzhou, 450001, China

    Haitao Wei

Authors
  1. Like Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shunyi Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenjing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haitao Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xia Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shunyi Zheng.

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

Zhao, L., Zheng, S., Yang, W. et al. An image thresholding approach based on Gaussian mixture model. Pattern Anal Applic 22, 75–88 (2019). https://doi.org/10.1007/s10044-018-00769-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-018-00769-w

Keywords

Search

Navigation