Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Level Set Formulation Based on Edge and Region Information with Application to Accurate Lesion Segmentation of Brain Magnetic Resonance Images

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

Magnetic resonance images have great significance for doctors’ analysis and diagnosis of diseases. One difficulty in segmenting magnetic resonance images is associated with the intensity inhomogeneity. In this paper, we propose an improved active contour model combining local and global information dynamically to segment images with intensity inhomogeneity. Besides, the atlas term is added into our energy functional, which improves the segmentation accuracy by restricting the segmented range around the location of the given atlas and making the contour move toward a position near the atlas. In this paper, we first present the multi-phase formulation of our model. Then, our model is applied to segment a total of 35 different brain magnetic resonance images with lesions. We also compare the performance of our model with other models, which can handle inhomogeneous images to some extent. Experimental results demonstrate that our model has promising performance for these challenging brain magnetic resonance images. Accuracy, efficiency and robustness of the proposed model have also been demonstrated by the numerical results and comparisons with other models.

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
Fig. 9

Similar content being viewed by others

References

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

    Article  MATH  Google Scholar 

  2. Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50(3), 271–293 (2002)

    Article  MATH  Google Scholar 

  3. Wang, L., Chen, G.Q., Shi, D., Chang, Y., Chan, S.X., Pu, J.T., Yang, X.D.: Active contours driven by edge entropy fitting energy for image segmentation. Signal Process. 149, 27–35 (2018)

    Article  Google Scholar 

  4. Ma, W.Y., Manjunath, B.: Edgeflow: A technique for boundary detection and image segmentation. IEEE Trans. Image Process. 9(8), 1507–1520 (2000)

    MathSciNet  MATH  Google Scholar 

  5. Arifina, A.Z., Asano, A.: Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recogn. Lett. 27(13), 1515–1521 (2006)

    Article  Google Scholar 

  6. Chang, Y.L., Li, X.: Adaptive image region-growing. IEEE Trans. Image Process. 3(6), 868–872 (1994)

    Article  Google Scholar 

  7. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  9. Li, C., Kao, C.Y., Gore, J., Ding, Z.: Implicit active contours driven by local binary fitting energy. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–7. IEEE Computer Society, Washington, DC, USA (2007)

  10. Li, C., Kao, C.Y., Gore, J., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Tian, D., Fan, L.N.: A brain MR images segmentation method based on SOM neural network. International Conference on Bioinformatics and Biomedical Engineering, pp. 686–689 (2007)

  12. Moeskops, P., Viergever, M.A.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2017)

    Article  Google Scholar 

  13. Ma, C., Luo, G., Wang, K.: Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Trans. Med. Imaging. https://doi.org/10.1109/TMI.2018.2805821 (2018)

  14. Ilunga-Mbuyamba, E., Avina-Cervantes, J., Cepeda-Negrete, J., Ibarra-Manzano, M., Chalopin, C.: Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Comput. Biol. Med. 91, 69–79 (2017)

    Article  Google Scholar 

  15. Koch, L.M., Rajchl, M., Bai, W., Baumgartner, C.F., Tong, T., Passeratpalmbach, J., Aljabar, P., Rueckert, D.: Multi-atlas segmentation using partially annotated data: methods and annotation strategies. IEEE Trans. Pattern Anal. Mach. 40(7), 1683–1696 (2018)

    Article  Google Scholar 

  16. Huo, J., Wu, J., Cao, J.W., Wang, G.H.: Supervoxel based method for multi-atlas segmentation of brain MR images. Neuroimage 175, 201–214 (2018)

    Article  Google Scholar 

  17. Del Re, E.C., Gao, Y., Eckbo, R., Petryshen, T.L., Blokland, G.A.M., Seidman, L.J., Konishi, J., Goldstein, J.M., Mccarley, R.W., Shenton, M.E.: A new MRI masking technique based on multi-atlas brain segmentation in controls and schizophrenia: a rapid and viable alternative to manual masking. J. Neuroimaging 26(1), 28–36 (2016)

    Article  Google Scholar 

  18. Iglesias, J.E., Sabuncu, M.R., Van Leemput, K.: A unified framework for cross-modality multi-atlas segmentation of brain MRI. Med. Image Anal. 17(8), 1181–1191 (2013)

    Article  Google Scholar 

  19. Pratondo, A., Chui, C.K., Ong, S.H.: Integrating machine learning with region-based active contour models in medical image segmentation. J. Vis. Commun. Image Represent. 43, 1–9 (2017)

    Article  Google Scholar 

  20. Wang, L., Li, C., Sun, Q., Xia, D., Kao, C.Y.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. J. Comput. Med. Imaging Graph. 33(7), 520–531 (2009)

    Article  Google Scholar 

  21. Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  22. Goldstein, T., Osher, S.: The split Bregman method for L1 regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Goldstein, T., Bresson, X., Osher, S.: Geometric applications of the split Bregman method: segmentation and surface reconstruction. SIAM J. Appl. Math. 45(1–3), 272–293 (2009)

    MathSciNet  MATH  Google Scholar 

  24. Yang, Y., Li, C., Kao, C.Y., Osher, S.: Split Bregman method for minimization of region-scalable fitting energy for image segmentation. In: Proceedings of International Symposium on Visual Computing, vol. 6454 LNCS, pp. 117–128. Las Vegas, Nevada, USA (2010)

  25. Yang, Y., Wu, B.: Convex image segmentation model based on local and global intensity fitting energy and split Bregman method. J. Appl. Math. 2012, Article ID 692,589 (2012)

  26. Yang, Y., Wu, B.: Split Bregman method for minimization of improved active contour model combining local and global information dynamically. J. Math. Anal. Appl. 389(1), 351–366 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  27. Yang, Y., Shu, X., Zhong, S.: Active contour model combining local and global information dynamically with application to segment brain MR images. In: Proceedings of 2017 International Conference on Biometrics Engineering and Application (ICBEA 2017), vol. Part F128052, pp. 45–49. Hong Kong (2017)

  28. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.A.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  29. Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A novel content-based active contour model for brain tumor segmentation. Magn. Reson. Imaging 30(5), 694–715 (2012)

    Article  Google Scholar 

  30. Weiss, N., Rueckert, D., Rao, A.: Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. In: 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Lecture Notes in Computer Science, vol 8149. Nagoya Univ, Nagoya, JAPAN, pp. 735–742 (2013)

  31. Mechrez, R., Goldberger, J., Greenspan, H.: Patch-based segmentation with spatial consistency: application to MS lesions in brain MRI. Int. J. Biomed. Imaging 2016(1), Article ID 7952 541

Download references

Acknowledgements

This work is supported by Shenzhen Fundamental Research Plan (No. JCYJ20160505175141489).

Author information

Authors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Yunyun Yang, Wenjing Jia & Xiu Shu

  2. Harbin Institute of Technology, Harbin, China

    Boying Wu

Authors
  1. Yunyun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjing Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiu Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Boying Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunyun Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Jia, W., Shu, X. et al. Level Set Formulation Based on Edge and Region Information with Application to Accurate Lesion Segmentation of Brain Magnetic Resonance Images. J Optim Theory Appl 182, 797–815 (2019). https://doi.org/10.1007/s10957-018-01451-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-018-01451-1

Keywords

Search

Navigation