Gradient Competition Anisotropy for Centerline Extraction and Segmentation of Spinal Cords

  • Max W. K. Law
  • Gregory J. Garvin
  • Sudhakar Tummala
  • KengYeow Tay
  • Andrew E. Leung
  • Shuo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


Centerline extraction and segmentation of the spinal cord – an intensity varying and elliptical curvilinear structure under strong neighboring disturbance are extremely challenging. This study proposes the gradient competition anisotropy technique to perform spinal cord centerline extraction and segmentation. The contribution of the proposed method is threefold – 1) The gradient competition descriptor compares the image gradient obtained at different detection scales to suppress neighboring disturbance. It reliably recognizes the curvilinearity and orientations of elliptical curvilinear objects. 2) The orientation coherence anisotropy analyzes the detection responses offered by the gradient competition descriptor. It enforces structure orientation consistency to sustain strong disturbance introduced by high contrast neighboring objects to perform centerline extraction. 3) The intensity coherence segmentation quantifies the intensity difference between the centerline and the voxels in the vicinity of the centerline. It effectively removes the object intensity variation along the structure to accurately delineate the target structure. They constitute the gradient competition anisotropy method which can robustly and accurately detect the centerline and boundary of the spinal cord. It is validated and compared using 25 clinical datasets. It is demonstrated that the proposed method well suits the applications of spinal cord centerline extraction and segmentation.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    Law, M.W.K., Chung, A.C.S.: An oriented flux symmetry based active contour model for three dimensional vessel segmentation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 720–734. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Olabarriaga, S.D., Breeuwer, M., Niessen, W.J.: Minimum cost path algorithm for coronary artery central axis tracking in CT images. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 687–694. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Benmansour, F., Cohen, L.: Tubular structure segmentation based on minimal path method and anisotropic enhancement. IJCV 92(2), 192–210 (2011)CrossRefGoogle Scholar
  5. 5.
    Bouix, S., Siddiqi, K., Tannenbaum, A.: Flux driven automatic centerline extraction. MedIA 9(3), 209–221 (2005)Google Scholar
  6. 6.
    Law, M.W.K., Chung, A.C.S.: Three dimensional curvilinear structure detection using optimally oriented flux. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 368–382. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Law, M.W.K., Chung, A.C.S.: Efficient implementation for spherical flux computation and its application to vascular segmentation. TIP 18(3), 596–612 (2009)MathSciNetGoogle Scholar
  8. 8.
    Wörz, S., Rohr, K.: Segmentation and quantification of human vessels using a 3-D cylindrical intensity model. TMI 16(8), 1994–2004 (2007)Google Scholar
  9. 9.
    Law, M.W.K., Chung, A.C.S.: A deformable surface model for vascular segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 59–67. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Koh, J., Kim, T., Chaudhary, V., Dhillon, G.: Automatic segmentation of the spinal cord and the dural sac in lumbar MR images using gradient vector flow field. In: IEEE EMBS, pp. 3117–3120 (2010)Google Scholar
  11. 11.
    Chen, M., Carass, A., Cuzzocreo, J., Bazin, P.L., Reich, D., Prince, J.L.: Topology preserving automatic segmentation of the spinal cord in magnetic resonance images. In: IEEE ISBI. From Nano to Macro., pp. 1737–1740 (2011)Google Scholar
  12. 12.
    McIntosh, C., Hamarneh, G.: Spinal crawlers: Deformable organisms for spinal cord segmentation and analysis. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 808–815. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Horsfield, M., Sala, S., Neema, M., Absinta, M., Bakshi, A., Sormani, M., Rocca, M., Bakshi, R., Filippi, M.: Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: Application in multiple sclerosis. NeuroImage 50(2), 446–455 (2010)CrossRefGoogle Scholar
  14. 14.
    Coulon, O., Hickman, S., Parker, G., Barker, G., Miller, D., Arridge, S.: Quantification of spinal cord atrophy from magnetic resonance images via a b-spline active surface model. MRM 47(6), 1176–1185 (2002)CrossRefGoogle Scholar
  15. 15.
    Law, M.W.K., Tay, K., Leung, A., Garvin, G.J., Li, S.: Dilated divergence based scale-space representation for curve analysis. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 557–571. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Mirebeau, J.M.: Anisotropic fast-marching on cartesian grids using lattice basis reduction (2012) (Preprint)Google Scholar
  17. 17.
    Chan, T., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. App. Math. 66(5), 1632–1648 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Hassouna, M., Farag, A.: Multistencils fast marching methods: A highly accurate solution to the eikonal equation on cartesian domains. PAMI 29(9), 1563–1574 (2007)CrossRefGoogle Scholar
  19. 19.
    Bae, E., Yuan, J., Tai, X.C.: Global minimization for continuous multiphase partitioning problems using a dual approach. IJCV 92(1), 112–129 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Chan, T., Vese, L.: Active contours without edges. TIP 10(2), 266–277 (2001)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Max W. K. Law
    • 1
    • 2
  • Gregory J. Garvin
    • 2
    • 4
  • Sudhakar Tummala
    • 2
  • KengYeow Tay
    • 2
    • 3
  • Andrew E. Leung
    • 2
    • 3
  • Shuo Li
    • 1
    • 2
  1. 1.GE HealthcareCanada
  2. 2.University of Western OntarioLondonCanada
  3. 3.London Health Sciences CentreLondonCanada
  4. 4.St. Joseph’s Health Care LondonLondonCanada

Personalised recommendations