Skip to main content
SpringerLink
Log in

Automatic tongue image segmentation based on gradient vector flow and region merging

  • ICIC2010
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper presents a region merging-based automatic tongue segmentation method. First, gradient vector flow is modified as a scalar diffusion equation to diffuse the tongue image while preserving the edge structures of tongue body. Then the diffused tongue image is segmented into many small regions by using the watershed algorithm. Third, the maximal similarity-based region merging is used to extract the tongue body area under the control of tongue marker. Finally, the snake algorithm is used to refine the region merging result by setting the extracted tongue contour as the initial curve. The proposed method is qualitatively tested on 200 images by traditional Chinese medicine practitioners and quantitatively tested on 50 tongue images using the receiver operating characteristic analysis. Compared with the previous active contour model-based bi-elliptical deformable contour algorithm, the proposed method greatly enhances the segmentation performance, and it could reliably extract the tongue body from different types of tongue images.

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

Similar content being viewed by others

References

  1. Maciocia G (1995) Tongue diagnosis in Chinese medicine. Washington

  2. Kirschbaum B (2000) Altas of Chinese tongue diagnosis. Washington

  3. Kass M, Witkin A, Terzopoulos D (1987) Snake: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  4. Zhang K, Zhang L, Song H, Zhou W (2010) Active contours with selective local or global segmentation a new formulation and level set method. Image Vis Comput 28(4):668–676

    Article  Google Scholar 

  5. Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recognit 43(4):1199–1206

    Article  MATH  Google Scholar 

  6. McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108

    Article  Google Scholar 

  7. He L, Peng Z, Everding B et al (2008) A comparative study of deformable contour methods on medical image segmentation. Image Vis Comput 26(2):141–163

    Article  Google Scholar 

  8. Felzenszwalb P, Huttenlocher D (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181

    Article  Google Scholar 

  9. Peng B, Zhang L, Yang J (2009) Iterated graph cuts for image segmentation. Asian Conf Comput Vis 5995:677–686

    Google Scholar 

  10. Zhang J, Wang Y, Shi X (2009) An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node’s shape assessment. Comput Med Imaging Graph 33(8):602–607

    Article  Google Scholar 

  11. Chen J, Shapiro L (2008) Medical image segmentation via min s-t cuts with side constraints. Comput Med Imaging Graph. In: International conference on pattern recognition, USA, pp 1–4

  12. Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598

    Article  Google Scholar 

  13. Zhang L, Paul B (2002) Edge detection by scale multiplication in wavelet domain. Pattern Recognit Lett 23(14):1771–1784

    Article  MATH  Google Scholar 

  14. Paul B, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490

    Article  Google Scholar 

  15. Pang B, Zhang D, Wang K (2005) The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine. IEEE Trans Med Imaging 24(8):946–956

    Article  Google Scholar 

  16. Ning J, Zhang L, Zhang D, Wu C (2010) Interactive image segmentation by maximal similarity based region merging. Pattern Recognit 43(2):445–456

    Article  MATH  Google Scholar 

  17. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–363

    Article  MathSciNet  MATH  Google Scholar 

  18. Weickert J (2001) Efficient image segmentation using partial differential equations and morphology. Pattern Recognit 34(9):1813–1824

    Article  MATH  Google Scholar 

  19. Sergio EH, Kenneth EB, Yu Y (2005) Region merging using homogeneity and edge integrity for watershed-based image segmentation. Opt Eng 44:017004

    Article  Google Scholar 

  20. Ojala T, Pietikäinen M, Topi Mäenpää (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  21. Guo Z, Zhang L, Zhang D (to appear) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process

  22. Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit 43(3):706–719

    Article  MATH  Google Scholar 

  23. Metz C (1978) Basic principles of ROC analysis. Semin Nucl Med 8:283–298

    Article  Google Scholar 

  24. Loizou C, Pattichis C, Pantziaris M, Nicolaides A (2007) An integrated system for the segmentation of atherosclerotic carotid plaque. IEEE Trans Inform Tech Biomed 11(5):661–667

    Article  Google Scholar 

  25. Fleiss JL, Cohen J, Everitt BS (1969) Large sample standard errors of kappa and weighted kappa. Psychol Bull 72(5):323–327

    Article  Google Scholar 

  26. Rosenfield GH, Fitzpatrick Lins K (1986) A coefficient of agreement as a measure of thematic classification accuracy. Photogramm Eng Remote Sens 52(2):223–227

    Google Scholar 

Download references

Acknowledgments

Authors thank sincerely Bo Huang, Li Liu and Zhenhua Guo for the great help in this paper. This work is partially supported by the National Science Foundation of China (NSFC) Key Overseas Project under Grant No. 60620160097 and the National Science Foundation of China (NSFC) under Grant No. 61003151 and the Fundamental Research Funds for the Central Universities under Grant No. QN2009091 and Northwest A & F University Research Foundation under Grant No. Z111020902.

Author information

Authors and Affiliations

  1. College of Information Engineering, Northwest A&F University, Yangling, People’s Republic of China

    Jifeng Ning

  2. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, People’s Republic of China

    Jifeng Ning & Chengke Wu

  3. Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

    Jifeng Ning, David Zhang & Feng Yue

Authors
  1. Jifeng Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengke Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jifeng Ning.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ning, J., Zhang, D., Wu, C. et al. Automatic tongue image segmentation based on gradient vector flow and region merging. Neural Comput & Applic 21, 1819–1826 (2012). https://doi.org/10.1007/s00521-010-0484-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0484-3

Keywords

Search

Navigation