Active Surface Approach for Extraction of the Human Cerebral Cortex from MRI

  • Simon F. Eskildsen
  • Lasse R. Østergaard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Segmentation of the human cerebral cortex from MRI has been subject of much attention during the last decade. Methods based on active surfaces for representing and extracting the cortical boundaries have shown promising results. We present an active surface method, that extracts the inner and outer cortical boundaries using a combination of different vector fields and a local weighting method based on the intrinsic properties of the deforming surface. Our active surface model deforms polygonal meshes to fit the boundaries of the cerebral cortex using a force balancing scheme. As a result of the local weighting strategy and a self-intersection constraint, the method is capable of modelling tight sulci where the image edge is missing or obscured. The performance of the method is evaluated using both real and simulated MRI data.


Outer Boundary Internal Force Fuzzy Membership Pressure Force Image Edge 
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.


  1. 1.
    Cohen, L.D., Cohen, I.: Finite-element methods for active contour models and balloons for 2D and 3D images. IEEE Trans. Pattern Analysis and Machine Intelligence (1993)Google Scholar
  2. 2.
    Xu, C., Pham, D.L., Rettmann, M.E., Yu, D.N., Prince, J.L.: Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Trans. Medical Imaging 18(6), 467–480 (1999)CrossRefGoogle Scholar
  3. 3.
    Zeng, X., Staib, L.H., Schultz, R.T., Duncan, J.S.: Segmentation and measurement of the cortex from 3-d mr images using coupled-surfaces propagation. IEEE Trans. Medical Imaging 18(10), 100–111 (1999)Google Scholar
  4. 4.
    Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis i: Segmentation and surface reconstruction. NeuroImage 9, 179–194 (1999)CrossRefGoogle Scholar
  5. 5.
    McInerney, T., Terzopoulos, D.: Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Trans. Medical Imaging 18(10), 840–850 (1999)CrossRefGoogle Scholar
  6. 6.
    MacDonald, D., Kabani, N., Avis, D., Evans, A.C.: Automated 3-D extraction of inner and outer suraces of cerebral cortex from mri. NeuroImage 12, 340–356 (2000)CrossRefGoogle Scholar
  7. 7.
    Han, X., Pham, D., Tosun, D., Rettmann, M., Xu, C., Prince, J.: Cruise: Cortical reconstruction using implicit surface evolution. NeuroImage 23(3), 997–1012 (2004)CrossRefGoogle Scholar
  8. 8.
    Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., Evans, A.C.: Automated 3-d extraction and evaluation of the inner and outer cortical surfaces using a laplacian map and partial volume effect classification. NeuroImage 27(1), 210–221 (2005)CrossRefGoogle Scholar
  9. 9.
    Eskildsen, S.F., Uldahl, M., Østergaard, L.R.: Extraction of the cerebral cortical boundaries from mri for measurement of cortical thickness. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 5747(2), 1400–1410 (2005)Google Scholar
  10. 10.
    Pham, D., Prince, J.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging 18(9), 737–752 (1999)CrossRefGoogle Scholar
  11. 11.
    Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J., Kabani, N., Holmes, C., Evans, A.: Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging 17(3), 463–468 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Simon F. Eskildsen
    • 1
  • Lasse R. Østergaard
    • 1
  1. 1.Dept. of Health Science and TechnologyAalborg UniversityDenmark

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