MICCAI 2004: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004 pp 368-375 | Cite as
Clustering Fiber Traces Using Normalized Cuts
Abstract
In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (Ncut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the Ncut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data. Segmentations are visualized as colored stream-tubes or transformed to a segmentation of voxel space, revealing structures in a way that looks promising for future explorative studies of diffusion weighted MRI data.
Keywords
White Matter Gaussian Kernel Weighted Undirected Graph Computer Vision Community Voxel SpaceReferences
- 1.Basser, P.J.: Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR in Biomedicine 8, 333–344 (1995)CrossRefGoogle Scholar
- 2.Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000)CrossRefGoogle Scholar
- 3.Behrens, T.E.J., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A.M., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience 6(7), 750–757 (2003)CrossRefGoogle Scholar
- 4.Brun, H.-J.P., Knutsson, H., Westin, C.-F.: Coloring of DT-MRI fiber traces using laplacian eigenmaps. In: Moreno-Díaz Jr., R., Pichler, F. (eds.) EUROCAST 2003. LNCS, vol. 2809, pp. 518–529. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 5.Catani, M., Howard, R.J., Pajevic, S., Jones, D.K.: Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17, 77–94 (2002)CrossRefGoogle Scholar
- 6.Ding, Z., Gore, J.C., Anderson, A.W.: Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Mag. Reson. Med. 49, 716–721 (2003)CrossRefGoogle Scholar
- 7.Fowlkes, C., Belongie, S., Malik, J.: Efficient Spatiotemporal Grouping Using the Nyström Method. In: CVPR, Hawaii (December 2001)Google Scholar
- 8.Gaffney, S.J., Smyth, P.: Curve clustering with random effects regression mixtures. In: Bishop, C.M., Frey, B.J. (eds.) Proc. Ninth Int. Workshop on AI and Stat. Florida (2003)Google Scholar
- 9.Lebihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., LavalJeantet, M.: MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161, 401–407 (1986)Google Scholar
- 10.Shi, J., Malik, J.: Normalized Cust and Image Segmentation. PAMI 22(8) (2000)Google Scholar
- 11.Shimony, J.S., Snyder, A.Z., Lori, N., Conturo, T.E.: Automated fuzzy clustering of neur-Sonal pathways in diffusion tensor tracking. In: Proc. Intl. Soc. Mag. Reson. Med. Honolulu, Hawaii, May 2002, vol. 10 (2002)Google Scholar
- 12.E.Weisstein: EricWeisstein’s world of mathematics (March 5, 004), http://mathworld.wolfram.com/
- 13.Westin, C.-F., Maier, S.E., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R.: Processing and Visualization of Diffusion Tensor MRI. Medical Image Analysis 6(8) (2002)Google Scholar
- 14.Zhang, S., Curry, T., Morris, D.S., Laidlaw, D.H.: Streamtubes and streamsurfaces for visualizing diffusion tensor MRI volume images. In: Visualization 2000 (October 2000)Google Scholar