Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Active contour evolved by joint probability classification on Riemannian manifold

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

Abstract

In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages 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

Similar content being viewed by others

References

  1. Kass, M., Witkin, A., Terzopoulos, D.: Snake: active contours model. Int. J. Comput. Vis. 1(3), 321–331 (1988)

  2. Cohen, L.: On active contour models and balloons. Comput. Vis. Graph. Image Process. 53, 211–218 (1991)

    MATH  Google Scholar 

  3. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comp. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  4. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  5. Chan, T., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66, 1632–1648 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bresson, X., Esedo-glu, S., Vandergheynst, P., Thiran, J., Osher, S.: Fast global minimization of the active contour/snake model. J. Math. Imaging Vis. 28, 151–167 (2007)

    Article  MathSciNet  Google Scholar 

  7. Li, C., Kao, C., Gore, J., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Image Process. 17, 1940–1949 (2008)

    Article  MathSciNet  Google Scholar 

  8. Li, C., Kao, C., Gore, J., Ding, Z.: 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)

    Article  MathSciNet  Google Scholar 

  9. Appia, V.V.B.: Non-local active contours. SIAM J. Imaging Sci. 5(3), 1022–1054 (2012)

    Article  MathSciNet  Google Scholar 

  10. Soganli, A., Uzunbas, M., Cetin, M.: Combining learning-based intensity distributions with nonparametric shape priors for image segmentation. Signal Image Video Process. 8(4), 789–798 (2014)

    Article  Google Scholar 

  11. Yezzi, A., Tsai, A., Willsky, A.: A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 13, 195–216 (2002)

    Article  Google Scholar 

  12. Unal, G., Yezzi Jr., A., Krim, H.: Information-theoretic active polygons for unsupervised texture segmentation. Intl J. Comput. Vis. 62(3), 199–220 (2002)

    Article  Google Scholar 

  13. Gokcay, E., Principe, J.C.: Information theoretic clustering. IEEE Trans. Pattern Anal. Mach. Intell. 24, 158–171 (2002)

    Article  Google Scholar 

  14. Kim, J., Fisher, J., Yezzi, A., Cetin, M., Willsky, A.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. Image Process. 14(10), 1486–1502 (2005)

    Article  MathSciNet  Google Scholar 

  15. Michaelovich, O., Rathi, Y., Tannenbaum, A.: Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans. Image Process. 16(11), 2787–2801 (2007)

    Article  MathSciNet  Google Scholar 

  16. Wu, H., Appia, V., Yezzi, A.: Numerical conditioning problems and solutions for nonparametric i.i.d statistical active contours. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1298–1311 (2013)

    Article  Google Scholar 

  17. Sochen, N., Kimmel, R., Malladi, R.: A general framework for low level vision. IEEE Trans. Image Process. 7(3), 310–318 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  18. Liu, J., Leung, S.: A splitting algorithm for image segmentation on manifolds represented by the grid based particle method. J. Sci. Comput. 56(2), 243–266 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  19. Goldstein, T., Bresson, X., Osher, S.: Geometric applications of the split Bregman method: segmentation and surface reconstruction. J. Sci. Comput. 45(1), 272–293 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ge, Q., Xiao, L., Zhang, J., Wei, Z.: An improved region-based model with local statistical features for image segmentation. Pattern Recognit. 45(4), 1578–1590 (2012)

    Article  MATH  Google Scholar 

  21. Sandhua, R., Georgioub, T., Tannenbauma, A.: A new distribution metric for image segmentation. Int. Soc. Opt. Photonic Med. Imaging (SPIE) 6914(1), 691404–691404 (2008)

    Google Scholar 

  22. Ni, K., Bresson, X., Chan, T., Esedoglu, S.: Local histogram based segmentation using the Wasserstein distance. Int. J. Comput. Vis. 84(1), 97–111 (2009)

  23. HouHou, N., Philippe, J.: Fast texture segmentation based on semi-local region descriptor and active contour. Numer. Math. Theory Methods Appl. 2(4), 445–468 (2009)

    MathSciNet  MATH  Google Scholar 

  24. Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13, 856–876 (2001)

    Article  Google Scholar 

  25. Osareh, A., Shadgar, B.: An automated tracking approach for extraction of retinal vasculature in fundus images. J. Opthalmic Vis. Res. 5, 20–26 (2010)

    Google Scholar 

  26. Dizdaroǧlu, B., Ataer-Cansizoglu, E., Kalpathy-Cramer, J., Katie, K., Chiang, M.F., Erdogmus, D.: Structure-based level set method for automatic retinal vasculature segmentation. EURASIP J. Image Video Process. 1, 1–26 (2014)

    Google Scholar 

  27. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  28. Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Natural Science Foundation Science Foundation of China under Grant (61502244, GZ215022, 61402239, 71301081), the Science Foundation of Jiangsu Province under Grant (BK20150859, BK20130868, BK20130877), the Science Foundation of Jiangsu Province University (15KJB520028), NJUPT Talent Introduction Foundation (NY213007), NJUPT Advanced Institute Open foundation (XJKY14012), China Postdoctoral Science Foundation (2015M580433, 2014M551637), Postdoctoral Science Foundation of Jiangsu Province (1401046C).

Author information

Authors and Affiliations

  1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

    Qi Ge & Shi-Peng Xie

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Fumin Shen

  3. Wuhan University, Wuhan, China

    Xiao-Yuan Jing

  4. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

    Xiao-Yuan Jing & Fei Wu

  5. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  6. School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden

    Hai-Bo Li

Authors
  1. Qi Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fumin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-Yuan Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shi-Peng Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dong Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hai-Bo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Ge.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ge, Q., Shen, F., Jing, XY. et al. Active contour evolved by joint probability classification on Riemannian manifold. SIViP 10, 1257–1264 (2016). https://doi.org/10.1007/s11760-016-0891-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0891-8

Keywords

Search

Navigation