A support vector machine based algorithm for magnetic resonance image segmentation

Proceedings - 4th International Conference on Natural Computation, ICNC 2008

Volumn 3, Issue , 2008, Pages 49-53

  Du, Xinyu ^a   Li, Yongjie ^a   Yao, Dezhong ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION TASKS; MAGNETIC RESONANCE IMAGES; MRI SEGMENTATIONS; REAL DATUMS; SIMULATED DATUMS; STRUCTURAL RISK MINIMIZATION PRINCIPLES; SUPERVISED CLASSIFICATIONS; SUPPORT VECTORS;

ATOMS; CLASSIFIERS; IMAGE PROCESSING; IMAGE RETRIEVAL; IMAGE SEGMENTATION; LEARNING SYSTEMS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; OPTIMIZATION; RESONANCE; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 57649120286     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2008.400     Document Type: Conference Paper

References (13)

1. Z. Liang, J.R. MacFall, and D.P. Harrington. Parameter estimation and tissue segmentation from multispectral MR images. IEEE. Trans. On Medical Imaging, 1994, 13:441-449.
2. Stuart Geman, Donald Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian of Gibbs random fields using simulated annealing. IEEE. Tran.on PAMI, 1984,6(6): 721-741
3. Chengyang Xu. Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images (Ph. D. thesis). John Hopkins Univ., 2000.
4. J.K.Udupa, L. Wei, S. Samarasekera, Y. Miki. Multiple sclerosis lession quatification using fuzzy-connectedness principle. IEEE Trans. On. Medical Imaging, 1997, 16(5):598-609.
5. Xiang Liao, Dezhong Yao, Dan Wu and Chaoyi Li. Combining spatial filters for the classification of single-trial EEG in a finger movement task. IEEE Trans BME, 54(5): 821-831, 2007
6. Vapnik, V. The Nature of Statistical Learning Theory. New York, NY: Springer-Verlag, 1995.
7. R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM, Journal of Machine Learning Research 6, 1889-1918, 2005.
8. http://www.bic.mni.mcgill.ca/brainweb/
9. Xiang Liao, Dezhong Yao and Chaoyi Li. Transductive SVM for reducing the training effort in BCI. J Neural Eng, 4: 246-254, 2007
10. Kecman, V.: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA (2001)
11. L.P. Wang (Ed.): Support Vector Machines: Theory and Application. Springer, Berlin (2005)
12. L.P. Wang, Fu, X.J.: Data Mining with Computational Intelligence, Springer, Berlin, 2005.
13. Ramirez, L.; Durdle, N.G.; Raso, V.J.; Hill, D.L.: A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography. IEEE Transactions on Information Technology in Biomedicine, Vol. 10, p.841, 2006.