Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Set Method

  • Li Wang
  • Feng Shi
  • John H. Gilmore
  • Weili Lin
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)


Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.


White Matter Gray Matter Segmentation Result Automatic Segmentation Neonatal Brain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Prastawa, M., Gilmore, J.H., Lin, W., Gerig, G.: Automatic segmentation of MR images of the developing newborn brain. Medical Image Analysis 9(5), 457–466 (2005)CrossRefGoogle Scholar
  2. 2.
    Xue, H., et al.: Automatic segmentation and reconstruction of the cortex from neonatal MRI. NeuroImage 38(3), 461–477 (2007)CrossRefGoogle Scholar
  3. 3.
    Shi, F., et al.: Neonatal brain image segmentation in longitudinal MRI studies. NeuroImage 49(1), 391–400 (2010)CrossRefGoogle Scholar
  4. 4.
    Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification. Medical Image Analysis 7(4), 43–55 (2000)CrossRefGoogle Scholar
  5. 5.
    Weisenfeld, N.I., Warfield, S.K.: Automatic segmentation of newborn brain MRI. NeuroImage 47(2), 564–572 (2009)CrossRefGoogle Scholar
  6. 6.
    Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis 7(4), 513–527 (2003)CrossRefGoogle Scholar
  7. 7.
    Gooya, A., Liao, H., Matsumiya, K., Masamune, K., Masutani, Y., Dohi, T.: A variational method for geometric regularization of vascular segmentation in medical images. IEEE Transactions on Image Processing 17(8), 1295–1312 (2008)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Xu, C., et al.: Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Trans. Med. Imag. 18(6), 467–480 (1999)CrossRefGoogle Scholar
  9. 9.
    Zeng, X., Staib, L., Schultz, R., Duncan, J.: Segmentation and measurement of the cortex from 3D MR images using coupled surfaces propagation. IEEE Trans. Med. Imag. 18(10), 100–111 (1999)Google Scholar
  10. 10.
    MacDonald, D., Kabani, N., Avis, D., Evans, A.C.: Automated 3-d extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage 12(3), 340–356 (2000)CrossRefGoogle Scholar
  11. 11.
    Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Cortex segmentation: a fast variational geometric approach. IEEE Trans. Med. Imag. 21(2), 1544–1551 (2002)CrossRefGoogle Scholar
  12. 12.
    Han, X., et al.: Cruise: Cortical reconstruction using implicit surface evolution. NeuroImage 23(3), 997–1012 (2004)CrossRefGoogle Scholar
  13. 13.
    Wang, L., He, L., Mishra, A., Li, C.: Active contours driven by local gaussian distribution fitting energy. Signal Processing 89(12), 2435–2447 (2009)CrossRefGoogle Scholar
  14. 14.
    Li, C., et al.: A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1083–1091. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Li, C., Kao, C., Gore, J., Ding, Z.: Implicit active contours driven by local binary fitting energy. In: CVPR, pp. 1–7 (2007)Google Scholar
  16. 16.
    Paragios, N.: A variational approach for the segmentation of the left ventricle in mr cardiac images. In: VLSM 2001 (2001)Google Scholar
  17. 17.
    Bresson, X., et al.: Fast global minimization of the active contour/snake model. J. Math. Imaging Vis. 28(2), 151–167 (2007)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Chan, T.F., Esedoglu, S., Nikolov, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66(5), 1632–1648 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Goldstein, T., Bresson, X., Osher, S.: Geometric applications of the split bregman method: Segmentation and surface reconstruction. CAM Report 09-06, UCLA (2009)Google Scholar
  20. 20.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Imag. Proc. 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  22. 22.
    Dice, L.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Li Wang
    • 1
    • 2
  • Feng Shi
    • 2
  • John H. Gilmore
    • 3
  • Weili Lin
    • 4
  • Dinggang Shen
    • 2
  1. 1.School of Computer Science & TechnologyNanjing University of Science and TechnologyChina
  2. 2.IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  3. 3.Department of PsychiatryUniversity of North Carolina at Chapel HillUSA
  4. 4.MRI Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

Personalised recommendations