Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1421–1429 | Cite as

A level set method for brain MR image segmentation under asymmetric distributions

  • Yunjie Chen
  • Menglin WuEmail author
Original Paper


Magnetic resonance (MR) image segmentation plays an essential role for brain disease diagnosis; however, suffered from low contrast, intensity inhomogeneity, noise and asymmetry of the intensity distribution, segmentation methods are always difficult to achieve satisfactory results. In this paper, we propose a novel level set method for brain MR image segmentation with consideration of these problems. We firstly generate a new region descriptor based on asymmetric Gaussian distributions in order to fit different shapes of observed nonsymmetric data. Secondly, we utilize the spatial distance and intensity similarity information of neighborhood pixels to extract local anisotropic spatial information to balance the noise reduction and detail preservation. After that, the extracted information and bias field information are combined to improve the asymmetric region descriptor utilized in the level set framework. Finally, we define a maximum likelihood energy functional on the whole image, integrating the local anisotropic spatial information, the bias field information and the asymmetric distributions. The experimental results on synthetic and clinical images demonstrated that our method can achieve desirable performance in spite of the severe noise and intensity inhomogeneity.


Anisotropic spatial information Asymmetric distribution Multi-phase level set Intensity inhomogeneity Magnetic resonance image segmentation 



This work was supported in part by the National Nature Science Foundation of China 61672291, 61701222, in part by the Nature Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 17KJB510026.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Proc. Comput. Sci. 102, 317–324 (2016)CrossRefGoogle Scholar
  2. 2.
    Wang, L., Shi, F., Yap, P.T., et al.: Longitudinally guided level sets for consistent tissue segmentation of neonates. Hum. Brain Mapp. 34(4), 956–972 (2013)CrossRefGoogle Scholar
  3. 3.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  4. 4.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gupta, D., Anand, R.S.: A hybrid edge-based segmentation approach for ultrasound medical images. Biomed. Signal Process. Control 31, 116–126 (2017)CrossRefGoogle Scholar
  6. 6.
    Suganthi, S.S., Ramakrishnan, S.: Anisotropic diffusion filter based edge enhancement for segmentation of breast thermogram using level sets. Biomed. Signal Process. Control 10(1), 128–136 (2014)CrossRefGoogle Scholar
  7. 7.
    Wang, X.F., Min, H., Zhang, Y.G.: Multi-scale local region based level set method for image segmentation in the presence of intensity inhomogeneity. Neurocomputing 151, 1086–1098 (2015)CrossRefGoogle Scholar
  8. 8.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
  9. 9.
    Li, C., Kao, C.Y., Gore, J.C., et al.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Li, C., Huang, R., Ding, Z., et al.: 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)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, Li, Chen, Yunjie, Ding, Zhaohua, Xia, Deshen: Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy. J. Neurosci. Methods 188(2), 316–325 (2010)CrossRefGoogle Scholar
  12. 12.
    Wang, Li, Shi, Feng, Lin, Weili, Gilmore, John H., Shen, Dinggang: Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58, 805–817 (2011)CrossRefGoogle Scholar
  13. 13.
    Chen, Y., Zhao, B., Zhang, J., et al.: Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model. Magn. Reson. Imag. 32(7), 941–955 (2014)CrossRefGoogle Scholar
  14. 14.
    Zhang, K., Zhang, L., Lam, K.M., et al.: A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans. Cybern. 46(2), 546–557 (2015)CrossRefGoogle Scholar
  15. 15.
    Meng, X., Gu, W., Chen, Y., et al.: Brain MR image segmentation based on an improved active contour model. PLoS ONE 12(8), e0183943 (2017)CrossRefGoogle Scholar
  16. 16.
    Brox, T.: From pixels to regions: partial differential equations in image analysis, Ph.D. dissertation, Dept. Comput. Sci., Saarland University, Saarbrücken, Germany (2005)Google Scholar
  17. 17.
    Nguyen, T.M., Jonathan Wu, Q.M., Mukherjee, D., Zhang, H.: A Bayesian bounded asymmetric mixture model with segmentation application. IEEE J. Biomed. Health Inform. 18, 109–119 (2014)CrossRefGoogle Scholar
  18. 18.
    Xu, Y., Géraud, T., Bloch, I.: From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning. In: IEEE International Conference on Image Processing (ICIP), pp. 4417–4421 (2017)Google Scholar
  19. 19.
    You, X., Peng, Q., Yuan, Y., et al.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn. 44(10–11), 2314–2324 (2011)CrossRefGoogle Scholar
  20. 20.
    Khowaja, S.A., Khuwaja, P., Ismaili, I.A.: A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification. SIViP 13, 379–387 (2019)CrossRefGoogle Scholar
  21. 21.
    Huang, L., Zhao, Y., Yang, T.: Skin lesion segmentation using object scale-oriented fully convolutional neural networks. SIViP 13, 431–438 (2019)CrossRefGoogle Scholar
  22. 22.
    Wells III, W.M., Grimson, W.E.L., Kikinis, R., et al.: Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 15(4), 429–442 (1996)CrossRefGoogle Scholar
  23. 23.
    Caldairou, B., Passat, N., Habas, P.A., et al.: A non-local fuzzy segmentation method: application to brain MRI. Pattern Recogn. 44(9), 1916–1927 (2011)CrossRefGoogle Scholar
  24. 24.
    Chen, Y., Zhang, H., Zheng, Y., et al.: An improved anisotropic hierarchical fuzzy c-means method based on multivariate student t-distribution for brain MRI segmentation. Pattern Recognit. 60(C), 778–792 (2016)CrossRefGoogle Scholar
  25. 25.
    Coupé, P., Yger, P., Prima, S., et al.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imag. 27(4), 425–441 (2008)CrossRefGoogle Scholar
  26. 26.
    Li, C., Gatenby, C., Wang, L., Gore, J.C.: A robust parametric method for bias field estimation and segmentation of MR images. In: CVPR 2009, pp. 218–223Google Scholar
  27. 27.
    Niu, S., Chen, Q., Sisternes, L.D., et al.: Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn. 61, 104–119 (2016)CrossRefGoogle Scholar
  28. 28.
    Shi, F., Wang, L., Dai, Y., et al.: LABEL: pediatric brain extraction using learning-based meta-algorithm. Neuroimage 62(3), 1975–1986 (2012)CrossRefGoogle Scholar
  29. 29.
    Van Leemput, K., Maes, F., Vandermeulen, D., et al.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 885–896 (1999)CrossRefGoogle Scholar
  30. 30.
    Li, C., Gore, J.C., Davatzikos, C.: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Mag. Reson. Imag. 32(7), 913–923 (2014)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Math and StatisticsNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Computer Science and TechnologyNanjing Tech UniversityNanjingChina

Personalised recommendations