Segmentation of multiple sclerosis lesions using support vector machines

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5032 I, Issue , 2003, Pages 16-26

  Ferrari, Ricardo J ^a,c   Wei, Xingchang ^a,b,c   Zhang, Yunyan ^a,b,c   Scott, James N ^b   Mitchell, J Ross ^a,b,c  

^a UNIVERSITY OF CALGARY   (Canada)

^b Radiology and Clinical Neurosciences   (Canada)

^c FOOTHILLS HOSPITAL   (Canada)

Author keywords

MRI; Multiple sclerosis; Segmentation of multiple sclerosis; Support vector machines

Indexed keywords

ALGORITHMS; FACE RECOGNITION; MAGNETIC RESONANCE IMAGING; NEUROLOGY; RADIAL BASIS FUNCTION NETWORKS;

VECTOR MACHINES;

DISEASES;

EID: 0042376130     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.481377     Document Type: Conference Paper

References (23)

1. A. Compston and A. Coles, "Multiple sclerosis.," Lancet, vol. 359, pp. 1221-1231, 2002.
2. M. Fillipi, V. Dousset, H. F. MacFarland, D. H. Miller, and R. I. Grossman, "Role of magnetic resonanceimaging in the diagnosis and monitoring of multiple sclerosis: consensus report of the white matter study group," Journal of Magnetic Resonance Imaging, vol. 15, pp. 499-504, 2002.
3. X. Wei, S. K. Warfield, K. H. Zou, Y. Wu, X. Li, A. Guimond, J. P. M. III, R. R. Benson, L. Wolfson, H. L. Winer, and C. R. G. Guttmann, "Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy," Journal of Magnetic Resonance Imaging, vol. 15, pp. 203-209, 2002.
4. A. Zijdenbos, R. Forghani, and A. Evans, "Automatic quantification of MS lesions in 3D MRI brain data sets:validation of INSPECT," presented at Medical image computing and computer-assisted intervention -MICCAI'98, Cambridge, MA, 1998.
5. J. K. Udupa and L. Wei, "Multiple sclerosis lesion quantification using fuzzy-connectedness principles," IEEE Transaction on Image Processing, vol. 16, pp. 598-609, 1997.
6. A. P. Zijdenbos and B. M. Dawant, "Brain segmentation and white matter lesion detection in MR images," Crit Rev Biomedical Engineering, vol. 22, pp. 401-465, 1994.
7. S. Warfield, J. Dengler, J. Zaers, C. R. G. Guttmann, W. M. W. III, G. J. Ettinger, J. Killer, and R. Kikinis, "Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions," The Journal of Image Guided Surgery, vol. 1, 1995.
8. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2 edition ed: Wiley-Interscience, 2001.
9. V. N. Vapnik, The Nature of Statistical Learing Theory. New York: Springer-Verlag, 1995.
10. C. Burges, "A tutorial on support vector machines for pattern recognition," in Data Mining and Knowledge Discovery, vol. 2. Boston: Kluwer Academic Publishers, 1998.
11. J. R. Mitchell, C. J. Jones, S. Karlik, K. Kennedy, D. Lee, and B. Rutt, "MR Multispectral Analysis of Multiple Sclerosis Lesions," The Journal of Magnetic Resonance Imaging, vol. 7, pp. 499-511, 1997.
12. D. G. Nishimura, Principles of Magnetic Resonance Imaging, Stanford: Stanford University Press, 1996.
13. M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger, "Robust Anisotropic Diffusion," IEEE Transaction on Image Processing, vol. 7, pp. 421-432, 1998.
14. J. B. Arnold, J.-S. Liow, K. Schaper, J. J. Stern, J. G. Sled, D. W. Shattuck, A. J. Worth, M. S. Cohen, R. M. Leahy, J. C. Mazziotta, and D. A. Rottenberg, "Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects," NeuroImage, vol. 13, pp. 931-943, 2001.
15. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data," IEEE Transaction on Medical Imaging, vol. 17, pp. 87-97, 1998.
16. R. E. Wendt, "Automatic adjustment of contrast and brightness of magnetic resonance images," Journal of Digital Imaging, vol. 7, pp. 95-97, 1994.
17. L. G. Nyul and J. K. Udupa, "On standardizing the MR image intensity scale," Magnetic Resonance in Medicine, vol. 42, pp. 1072-1081, 1999.
18. J. R. Mitchell, S. J. Karlik, D. H. Lee, and A. Fenster, "Computer assisted identification and quantification of multiple sclerosis lesions, in MR imaging volumes in the brain," The Journal of Magnetic Resonance Imaging, vol. 4, pp. 197-208, 1994.
19. C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, vol. 20, pp. 1-25, 1995.
20. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernel-based learning methods. United Kingdom: Cambridge University Press, 2000.
21. C. C. Chang and C. J. Lin, "Training v-Support Vector Classifiers: Theory and Algorithms," Neural Computation, vol. 13, pp. 2119-2147, 2001.
22. C. Burges, "Simplified support vector decision rules," presented at 13th International Conference on Machine Learning, 1996.
23. T. Downs, K. E. Gates, and A. Masters, "Exact simplification of Support Vector Solutions," Journal of Machine Learning, vol. 2, pp. 293-297, 2001.