Classification in DTI using shapes of white matter tracts

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Volumn , Issue , 2009, Pages 2719-2722

  Adluru, Nagesh ^a   Hinrichs, Chris ^d   Chung, Moo K ^d   Lee, Jee Eun ^d   Singh, Vikas ^d   Bigler, Erin D ^a   Lange, Nicholas ^b   Lainhart, Janet E ^c   Alexander, Andrew L ^d  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

^c UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^d UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION TENSOR IMAGING; GEOMETRY; MACHINERY; MAGNETIC RESONANCE IMAGING; POPULATION STATISTICS; TISSUE;

GEOMETRIC MODELING; PREDICTION ACCURACY; QUANTITATIVE INFORMATION; REGIONS OF INTEREST; SHAPE REPRESENTATION; SPATIAL NORMALIZATION; TENSOR ORIENTATION; WHITE MATTER TRACTS;

TENSORS;

EID: 77951016197     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2009.5333386     Document Type: Conference Paper

References (21)

1. M. Lazar, D. Weinstein, J. Tsuruda, K. Hasan, and et. al., "White matter tractography using diffusion tensor deflection," Human Brain Mapping, vol.18, no.4, 2003.
2. P. Savadjiev, J. S. Campbell, G. B. Pike, and K. Siddiqi, "Streamline flows for white matter fibre pathway segmentation in diffusion MRI," in MICCAI. Berlin, Heidelberg: Springer-Verlag, 2008, pp. 135-143.
3. M. D. King, D. G. Gadiana, and C. A. Clarka, "A random effects modelling approach to the crossing-fibre problem in tractography," NeuroImage, vol.44, no.3, pp. 753-768, February 2009.
4. L. O'Donnel, C.-F. Westin, and A. Golby, "Tract-based morphometry for white matter group analysis," NeuroImage, vol.45, pp. 832-844, 2009.
5. C. B. Goodlett, T. P. Fletcher, J. H. Gilmore, and G. Gerig, "Group analysis of DTI fiber tract statistics with application to neurodevelopment, " NeuroImage, vol.45, no.1, 2009.
6. H. Sun, P. Yushkevich, H. Zhang, P. Cook, J. Duda, T. Simon, and J. C. Gee, "Shape-based normalization of the corpus callosum for DTI connectivity analysis," TMI, vol.26, 2007.
7. M. Bastin, J. Piatkowski, A. Storkey, L. Brown, A. MacLullich, and J. Clayden, "Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing," NeuroImage, vol.43, pp. 20-28, 2008.
8. P. Yushkevich, H. Zhang, T. Simon, and J. Gee, "Structure-specific statistical mapping of white matter tracts using the continuous medial representation," in MMBIA07, 2007.
9. J. E. Lee, D. Hsu, A. Alexander, M. Lazar, E. Bigler, and J. Lainhart, "A study of underconnectivity in autism using DTI: W-matrix tractography," in ISMRM, 2008.
10. X. Ke, S. Hong, T. Tang, H. Huang, B. Zou, H. Li, Y. Hang, and Z. Lu, "An analysis method of the fiber tractography of corpus callosum in autism based on DTI data," in IEEE ICBBE, 2008, pp. 2409-2413.
11. S. Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.4, pp. 509-522, 2002.
12. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol.10, no.5, pp. 1299-1319, 1998.
13. S. Maji, A. C. Berg, and J. Malik, "Classification using intersection kernel support vector machines is efficient," in CVPR, 2008, pp. 1-8.
14. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol.20, no.3, 1995.
15. P. Vemuri, J. Gunter, M. Senjem, J. Whitwell, and et. al., "AD diagnosis in individual subjects using struct. MR images," Neu.Im., 2008.
16. Y. Fan, D. Shen, R. Gur, R. Gur, and C. Davatzikos, "COMPARE: Classification of morphological patterns using adaptive regional elements," Tran. Med. Imag., 2007.
17. V. Singh, L. Mukherjee, and M. Chung, "Cortical surface thickness as a classifier: Boosting for autism classification," in MICCAI, 2008, pp. 999-1007.
18. S. Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, and et. al., "Nonlinear component analysis as a kernel eigenvalue problem," Neu. Img., vol.31, pp. 1487-1505, 2006.
19. I. Jolliffe, Principal Component Analysis. Springer, 2002.
20. K. Diamantaras and S. Kung, Principal component neural networks: theory and applications. New York, NY, USA: John Wiley & Sons, Inc., 1996.
21. F. Pereira, T. Mitchell, and M. Botvinick, "Machine learning classifiers and fMRI: A tutorial overview," NeuroImage, vol.45, pp. S199-S209, March 2009.