Gradient Competition Anisotropy for Centerline Extraction and Segmentation of Spinal Cords
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.
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- 5.Bouix, S., Siddiqi, K., Tannenbaum, A.: Flux driven automatic centerline extraction. MedIA 9(3), 209–221 (2005)Google Scholar
- 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
- 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.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
- 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.Mirebeau, J.M.: Anisotropic fast-marching on cartesian grids using lattice basis reduction (2012) (Preprint)Google Scholar