Fully automated classification of HARDI in vivo data using a support vector machine

NeuroImage

Volumn 46, Issue 3, 2009, Pages 642-651

  Schnell, S ^a   Saur, D ^a   Kreher, B W ^a   Hennig, J ^a   Burkhardt, H ^b   Kiselev, V G ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Classification; Crossing fibre bundles; HARDI; Rotation invariant; Spherical harmonics; Support vector machine

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; ARTIFACT; DIFFUSION WEIGHTED IMAGING; HUMAN; HUMAN EXPERIMENT; IMAGE PROCESSING; NEURORADIOLOGY; NORMAL HUMAN; PATTERN RECOGNITION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SIGNAL NOISE RATIO; SUPPORT VECTOR MACHINE; WHITE MATTER;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; NERVE FIBERS, MYELINATED; NEURONS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 64949134048     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.03.003     Document Type: Article

References (27)

1. Alexander D.C. Multiple-fiber reconstruction algorithms for diffusion MRI. Ann. N.Y. Acad. Sci. 1064 (2005) 113-133
2. Alexander D.C., Barker G.J., et al. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48 2 (2002) 331-340
3. Arfken G.B., and Weber H.J. Spherical Harmonics" and "Integrals of the Products of Three Spherical Harmonics. Mathematical Methods For Physicists (1985), Elsevier Publishing Company, Academic Press, Orlando, USA 680-685 and 698-700
4. Basser P.J., Mattiello J., et al. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66 1 (1994) 259-267
5. Behrens T.E.J., Berg H.J., et al. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?. NeuroImage 34 (2007) 144-155
6. Burges C.J.C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. Boston, USA 2 (1998), Kluwer Academic Publishers 121-167
7. Cortes C., and Vapnik V. Support Vector Networks. Mach. Learn. 20 (1995) 273-297
8. Cristianini N., and John S.-T. An introduction to support vector machines: and other kernel-based learning methods (2000), Cambridge University Press
9. Descoteaux M., Angelino E., et al. Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn. Reson. Med. 56 (2006) 395-410
10. Frank L.R. Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 47 (2002) 1083-1099
11. Gottrup C., Thomsen K., et al. Applying instance-based techniques to prediction of final outcome in acute stroke. Artif. Intell. Med. 33 3 (2005) 223-236
12. Gualtieri J.A., and Cromp R.F. Support Vector Machines for Hyperspectral Remote Sensing Classification (1999), SPIE
13. Hasan K.M., and Narayana P.A. Retrospective measurement of the diffusion tensor eigenvalues from diffusion anisotropy and mean diffusivity in DTI. Magn. Reson. Med. 56 (2006) 130-137
14. Jones D.K., Horsfield M.A., et al. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42 3 (1999) 515-525
15. Kreher B.W., Schneider J.F., et al. Multitensor approach for analysis and tracking of complex fiber configurations. Magn. Reson. Med. 54 (2005) 1216-1225
16. Kreher B.W., Mader I., et al. Gibbs tracking: a novel approach for the reconstruction of neuronal pathways. Magn. Reson. Med. 60 4 (2008) 953-963
17. Mouridsen K., Christensen S., et al. Automatic selection of arterial input function using cluster analysis. Magn. Reson. Med. 55 (2006) 524-531
18. Nattkemper T.W. Multivariate image analysis in biomedicine - a methodological review. J. Biomed. Informatics 37 5 (2004) 380-391
19. Nieuwenhuys R., Voogd J., et al. The Human Central Nervous System (2008), Springer, Berlin Heidelberg New York
20. Quddus A., Fieguth P., et al. Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images (2005), IEEE Engineering in Medicine and Biology, Shanghai, China
21. Ronneberger, O., (2004). "libSVMtl." Albert-Ludwigs University Freiburg, Germany. from http://lmb.informatik.uni-freiburg.de/lmbsoft/libsvmtl/download.en.html.
22. Schölkopf B., and Smola A.J. Learning With Kernels (2002), MIT Press, Cambridge, Massachusetts (USA)
23. Tuch D.S., Weisskoff R.M., et al. High Angular Resolution Diffusion Imaging of the Human Brain (1999), International Annual Meeting of the International Society for Magnetic Resonance in Medicine, Philadelphia, USA
24. Webster A., and Szegö G. Leipzig and Berlin (1930), Teubner, Germany
25. Westin C.-F., Peled S., et al. Geometrical Diffusion Measures For Mri From Tensor Basis Analysis (1997), International Annual Meeting of the International Society for Magnetic Resonance in Medicine, Vancouver, Canada
26. Yeo, B.T.T., (2005). Computing Spherical Transform and Convolution on the 2-Sphere. 1-8.
27. Zaitsev M., Hennig J., et al. Geometric Distortions Applied to Diffusion Tensor Imaging (2006), International Annual Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, USA