Multi-phase level set method for precise segmentation and correction of brain MRI

Abstract

Medical image segmentation as an earlier application field in image segmentation is the key technology of medical image analysis and is also a key point and difficulty in clinical application. This paper proposes an accurate and robust active contour model based on the four-phase level set for medical MR images. First we define a new energy functional by combining the data term and the length term, where the data term is defined by transforming the energy functional of the multiplicative intrinsic component optimization (MICO) model into the level set framework after adding an edge detector function. Then, when we minimize the energy functional, we use the split Bregman method to improve the convergence speed. To test the performance of our model, we do lots of experiments according to the different brain MR images, which show that even under the severe influence of bias field or shadows, our model can still segment these images well, and our model is robust to the initial contours and noise. Moreover, our model is compared with the MICO model by experimental results and the numerical values, concluding that our model is better than the MICO model no matter in segmentation accuracy or in correction effect.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Akram, F., Angel Garcia, M., Puig, D.: Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity. PLoS One 12(4), Article ID: e0174813 (2017)

  2. 2.

    Akram, F., Kim, J.H., Ul Lim, H., Choi, K.N.: Segmentation of intensity inhomogeneous brain MR images using active contours. Comput. Math. Method Med. Article ID: 194614 (2014)

  3. 3.

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

    Article  Google Scholar 

  4. 4.

    Chu, Y.J., Mak, C.M.: A new QR decomposition-based RLS algorithm using the split Bregman method for L1-regularized problems. Signal Process. 128, 303–308 (2016)

    Article  Google Scholar 

  5. 5.

    Ding, K., Xiao, L., Weng, G.: Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Signal Process. 134, 224–233 (2017)

    Article  Google Scholar 

  6. 6.

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

    MathSciNet  Article  Google Scholar 

  7. 7.

    Hasan, A.M., Meziane, F., Aspin, R., Jalab, H.A.: Segmentation of brain tumors in MRI images using three-dimensional active contour without edge. Symmetry-Basel 8(11), 132 (2016)

    MathSciNet  Article  Google Scholar 

  8. 8.

    Heydari, M., Karami, M.R., Babahani, A.: A new adaptive coupled diffusion PDE for MRI Rician noise. Signal Image Video Process. 10(7), 1211–1218 (2016)

    Article  Google Scholar 

  9. 9.

    Juntu J., Sijbers J., Van Dyck D., Gielen J.: Bias field correction for MRI images. In: Kurzyński M., Puchała E., Woźniak M., żołnierek A. (eds.) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg (2005). https://link.springer.com/chapter/10.1007/3-540-32390-2_64#citeas

  10. 10.

    Li, C., Gore, J.C., Davatzikos, C.: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging 32(7), 913–923 (2014)

    Article  Google Scholar 

  11. 11.

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

    MathSciNet  Article  Google Scholar 

  12. 12.

    Likar, B., Viergever, M., Pernus, F.: Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans. Med. Imaging 20(12), 1398–1410 (2001)

    Article  Google Scholar 

  13. 13.

    Norouzi, A., Rahim, M.S.M., Altameem, A., Saba, T., Rad, A.E., Rehman, A., Uddin, M.: Medical image segmentation methods, algorithms, and applications. IETE Tech. Rev. 31(3), 199–213 (2014). https://doi.org/10.1080/02564602.2014.906861

    Article  Google Scholar 

  14. 14.

    Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460–489 (2005)

    MathSciNet  Article  Google Scholar 

  15. 15.

    Qiao, N., Zou, B.: A segmentation method for noisy photoelectric image. Optik 124(20), 4092–4094 (2013)

    Article  Google Scholar 

  16. 16.

    Shi, Y., Zhang, X., Liu, Z.: Automatic segmentation of hippocampal subfields based on multi-atlas image segmentation techniques. Signal Image Video Process. 31(2), 121–128 (2014)

    Google Scholar 

  17. 17.

    Tian, Y., Duan, F., Zhou, M., Wu, Z.: Active contour model combining region and edge information. Mach. Vis. Appl. 24(1), 47–61 (2013)

    Article  Google Scholar 

  18. 18.

    Tustison, N., Avants, B., Cook, P., Zheng, Y.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  19. 19.

    Uros, V., Franjo, P., Bostjan, L.: A review of methods for correction of intensity inhomogeneity in mri. IEEE Trans. Med. Imaging 26(3), 405–421 (2007)

    Article  Google Scholar 

  20. 20.

    Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26(3), 405–421 (2007)

    Article  Google Scholar 

  21. 21.

    Xu, J., Zhu, S., Soh, Y.C., Xie, L.: A bregman splitting scheme for distributed optimization over networks. IEEE Trans. Autom. Control 63(11), 3809–3824 (2018)

    MathSciNet  Article  Google Scholar 

  22. 22.

    Yang, Y., Li, C., Kao, C.Y., Osher, S.: Split Bregman method for minimization of region-scalable fitting energy for image segmentation. In: International Symposium on Visual Computing (ISVC), Lecture Notes in Computer Science, vol. 6454, pp. 117–128. Springer, Berlin, Heidelberg (2010)

  23. 23.

    Yang, Y., Tian, D., Wu, B.: A fast and reliable noise-resistant medical image segmentation and bias field correction model. Magn. Reson. Imaging 54, 15–31 (2018)

    Article  Google Scholar 

  24. 24.

    Yang, Y., Wenjing, J.: Improved level set model based on bias information with application to color image segmentation and correction. Signal Image Video Process. (2019). https://doi.org/10.1007/s11760-019-01472-x

    Article  Google Scholar 

  25. 25.

    Yang, Y., Zhao, Y., Wu, B.: Split Bregman method for minimization of fast multiphase image segmentation model for inhomogeneous images. J. Optim. Theory Appl. 166(1), 285–305 (2015)

    MathSciNet  Article  Google Scholar 

  26. 26.

    Yazdani, S., Yusof, R., Karimian, A., Pashna, M., Hematian, A.: Image segmentation methods and applications in mri brain images. IETE Tech. Rev. 32(6), 413–427 (2015)

    Article  Google Scholar 

  27. 27.

    Zhang, K., Zhang, L., Lam, K.M., Zhang, D.: A level set approach to image segmentation with intensity inhomogeneity. IEEE T. Cybern. 46(2), 546–557 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yunyun Yang.

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

Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Yang, Y. & Zhong, S. Multi-phase level set method for precise segmentation and correction of brain MRI. SIViP 15, 53–61 (2021). https://doi.org/10.1007/s11760-020-01724-1

Download citation

Keywords

  • Image segmentation
  • Bias correction
  • Split Bregman method
  • MR images

Mathematics Subject Classification

  • 90C47
  • 65K10
  • 49M37