Navegar

Colección

Datos Estadísticas

Artículo

Este artículo es de Acceso Abierto y puede ser descargado en su versión final desde nuestro repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper,we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles.

Registro:

Documento: Artículo
Título:Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
Autor:Wassermann, D.; Descoteaux, M.; Deriche, R.
Filiación:Odyssee Project Team, INRIA/ENPC/ENS INRIA, Sophia-Antipolis, 2004 Route des Lucioles, 06902 Sophia Antipolis, France
Computer Science Department, FCEyN, Ciudad Universitaria, C1428EGA Buenos Aires, Argentina
Palabras clave:Diffusion; Image segmentation; Magnetic resonance imaging; Neurology; Diffusion maps; Q-ball imaging segmentation; White matter fiber clustering; Medical imaging
Año:2008
Volumen:2008
Número:1
DOI: http://dx.doi.org/10.1155/2008/526906
Título revista:International Journal of Biomedical Imaging
Título revista abreviado:Int. J. Biomed. Imaging
ISSN:16874188
PDF:https://bibliotecadigital.exactas.uba.ar/download/paper/paper_16874188_v2008_n1_p_Wassermann.pdf
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16874188_v2008_n1_p_Wassermann

Referencias:

  • Alexander, D.C., Barker, G.J., Arridge, S.R., Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data (2002) Magnetic Resonance in Medicine, 48 (2), pp. 331-340
  • Anderson, A.W., Measurement of fiber orientation distributions using high-angular resolution diffusion imaging (2005) Magnetic Resonance in Medicine, 54 (5), pp. 1194-1206. , adam.anderson@vanderbilt.edu
  • Andrews, G., Askey, R., Roy, R., (1999) Special Functions, , Cambridge University Press Cambridge, UK
  • Anwander, A., Tittgemeyer, M., Von Cramon, D.Y., Friederici, A.D., Knosche, T.R., Connectivity-based parcellation of Broca's area (2007) Cerebral Cortex, 17 (4), pp. 816-825. , tittge@cbs.mpg.de
  • Basser, P.J., Jones, D.K., Diffusion-tensor MRI: Theory, experimental design and data analysis - A technical review (2002) NMR in Biomedicine, 15 (7-8), pp. 456-467
  • Belkin, M., Niyogi, P., Laplacian eigenmaps for dimensionality reduction and data representation (2003) Neural Computation, 15 (6), pp. 1373-1396. , misha@math.uchicago.edu niyogi@cs.uchicago.edu
  • Chung, F., (1997) Spectral Graph Theory, , CBMS-AMS
  • Coifman, R.R., Lafon, S., Lee, A.B., Maggioni, M., Nadler, B., Warner, F., Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps (2005) Proceedings of the National Academy of Sciences of the United States of America, 102 (21), pp. 7426-7431. , coifman-ronald@yale.edu
  • Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., Apparent diffusion coefficients from high-angular resolution diffusion imaging: Estimation and applications (2006) Magnetic Resonance in Medicine, 56 (2), pp. 395-410. , Maxime.Descoteaux@sophia.inria.fr
  • Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., Regularized, fast, and robust analytical $q$-ball imaging (2007) Magnetic Resonance in Medicine, 58 (3), pp. 497-510. , 2007
  • Descoteaux, M., Angelina, E., Fitzgibbons, S., Deriche, R., A fast and robust ODF estimation algorithm in Q-ball imaging (2006) Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2006, pp. 81-84. , Arlington, Va, USA
  • Descoteaux, M., Deriche, R., Segmentation of q-ball images using statistical surface evolution (2007) Medical Image Computing and Computer-Assisted Intervention (MICCAI '07), , Springer Brisbane, Australia
  • Donoho, D.L., Grimes, C., Image manifolds which are isometric to Euclidean space (2005) Journal of Mathematical Imaging and Vision, 23 (1), pp. 5-24. , donoho@stat.stanford.edu cgrimes@google.com
  • Fiedler, M., A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory (1975) Czechoslovak Mathematical Journal, 25, pp. 619-633
  • Frank, L.R., Characterization of anisotropy in high-angular resolution diffusion-weighted MRI (2002) Magnetic Resonance in Medicine, 47 (6), pp. 1083-1099
  • Fsl, (2006) Fmrib Software Library, , University of Oxford
  • Hansen, P., The l-curve and its use in the numerical treatment of inverse problems (2001) Computational Inverse Problems in Electrocardiology, pp. 119-142
  • Hess, C., Mukherjee, P., Han, E., Xu, D., Vigneron, D., Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis (2006) Magnetic Resonance in Medicine, 56, pp. 104-117
  • Jain, A.K., Murty, M.N., Flynn, P.J., Data clustering: A review (1999) ACM Computing Surveys, 31 (3), pp. 316-323
  • Jenkinson, M., Bannister, P., Brady, M., Smith, S., Improved optimization for the robust and accurate linear registration and motion correction of brain images (2002) NeuroImage, 17 (2), pp. 825-841
  • Jolliffe, I., (1986) Principal Component Analysis, , Springer Series in Statistics Berlin, Germany
  • Jonasson, L., Hagmann, P., Pollo, C., Bresson, X., Richero Wilson, C., Meuli, R., A level set method for segmentation of the thalamus and its nuclei in DT-MRI (2007) Signal Processing, 87 (2), pp. 309-321. , Lisa.Jonasson@epfl.ch. Special Issue of Signal Processing on Diffuision Tensor Imaging
  • Jones, D.K., Horsfield, M.A., Simmons, A., Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging (1999) Magnetic Resonance in Medicine, 42 (3), pp. 515-525
  • Jones, D.K., Williams, S.C.R., Gasston, D., Horsfield, M.A., Simmons, A., Howard, R., Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time (2002) Human Brain Mapping, 15 (4), pp. 216-230
  • Lafon, S., (2004) Diffusion Maps and Geometric Harmonics, , Yale University New Haven, Conn, USA
  • Lafon, S., Lee, A.B., Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization (2006) IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (9), pp. 1393-1403. , stephane.lafon@gmail.com annlee@stat.cmu.edu
  • Lenglet, C., Rousson, M., Deriche, R., DTI segmentation by statistical surface evolution (2006) IEEE Transactions on Medical Imaging, 25 (6), pp. 685-700. , clenglet@sophia.inria.fr
  • Lenglet, C., Rousson, M., Deriche, R., Faugeras, O., Statistics on the manifold of multivariate normal distributions: Theory and application to diffusion tensor MRI processing (2006) Journal of Mathematical Imaging and Vision, 25 (3), pp. 423-444. , rachid.deriche@sophia.inria.fr mikael.rousson@siemens.com christophe.lenglet@sophia.inria.fr olivier.faugeras@sophia.inria.fr
  • Nadler, B., Lafon, S., Coifman, R.R., Kevrekidis, I.G., Weiss, Y., Schölkopf, Y.B., Platt, J., Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators (2006) Advances in Neural Information Processing Systems 18, pp. 955-962. , Cambridge, MA, USAMIT Press
  • O'Donnell, L., Westin, C.-F., High-dimensional white matter atlas generation and group analysis (2006) Medical Image Computing and Computer-Assisted Intervention (MICCAI '06), 4191, pp. 243-251. , lauren@csail.mit.edu Copenhagen, DenmarkLecture Notes in Computer Science*Larsen R.*Nielsen M.*Sporring J. Springer
  • Pierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., Di Chiro, G., Diffusion tensor MR imaging of the human brain (1996) Radiology, 201 (3), pp. 637-648
  • Roweis, S.T., Saul, L.K., Nonlinear dimensionality reduction by locally linear embedding (2000) Science, 290 (5500), pp. 2323-2326
  • Shi, J., Malik, J., Normalized cuts and image segmentation (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), pp. 888-905
  • Talairach, J., Tournoux, P., (1988) Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System : An Approach to Cerebral Imaging, , Thieme Medical Publishers New York, NY, USA
  • Tenenbaum, J.B., De Silva, V., Langford, J.C., A global geometric framework for nonlinear dimensionality reduction (2000) Science, 290 (5500), pp. 2319-2323
  • Tishby, N., Slonim, N., Data clustering by Markovian relaxation and the information bottleneck method (2000) Neural Information Processing Systems (NIPS '00), , Denver, Colo, USA
  • Tournier, J.-D., Calamante, F., Gadian, D.G., Connelly, A., Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution (2004) NeuroImage, 23 (3), pp. 1176-1185. , D.Tournier@ich.ucl.ac.uk
  • Tuch, D.S., Q-ball imaging (2004) Magnetic Resonance in Medicine, 52 (6), pp. 1358-1372. , dtuch@nmr.mgh.harvard.edu
  • Wang, Z., Vemuri, B.C., DTI segmentation using an information theoretic tensor dissimilarity measure (2005) IEEE Transactions on Medical Imaging, 24 (10), pp. 1267-1277. , vemuri@cise.ufl.edu
  • Weiss, Y., Segmentation using eigenvectors: A unifying view (1999) Proceedings of the IEEE International Conference on Computer Vision (ICCV '99), 2, pp. 975-982. , Kerkyra, Greece
  • Zelnik-Manor, L., Perona, P., Self-tuning spectral clustering (2004) Advances in Neural Information Processing Systems, 17, pp. 1601-1608
  • Ziyan, U., Tuch, D., Westin, C.-F., Segmentation of thalamic nuclei from DTI using spectral clustering (2006) Medical Image Computing and Computer-Assisted Intervention (MICCAI '06), 4191, pp. 807-814. , david.tuch@novartis.com ulas@mit.edu westin@bwh.harvard.edu Lecture Notes in Computer Science Copenhagen, Denmark

Citas:

---------- APA ----------
Wassermann, D., Descoteaux, M. & Deriche, R. (2008) . Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation. International Journal of Biomedical Imaging, 2008(1).
http://dx.doi.org/10.1155/2008/526906
---------- CHICAGO ----------
Wassermann, D., Descoteaux, M., Deriche, R. "Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation" . International Journal of Biomedical Imaging 2008, no. 1 (2008).
http://dx.doi.org/10.1155/2008/526906
---------- MLA ----------
Wassermann, D., Descoteaux, M., Deriche, R. "Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation" . International Journal of Biomedical Imaging, vol. 2008, no. 1, 2008.
http://dx.doi.org/10.1155/2008/526906
---------- VANCOUVER ----------
Wassermann, D., Descoteaux, M., Deriche, R. Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation. Int. J. Biomed. Imaging. 2008;2008(1).
http://dx.doi.org/10.1155/2008/526906