Classification of pathological human brain lesions using magnetic resonance spectroscopy at 3T

IFMBE Proceedings

Volumn 25, Issue 4, 2009, Pages 1368-1370

  Dimou, I N ^a   Tsougos, I ^b   Tsolaki, E ^b   Theodorou, K ^b  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

^b UNIVERSITY OF THESSALY   (Greece)

Author keywords

Magnetic resonance spectroscopy; MRS; Support vector machines; SVM

Indexed keywords

BIOINFORMATICS; BIOMECHANICS; BIOMEDICAL ENGINEERING; BIOPHYSICS; BRAIN; DIAGNOSIS; MAGNETIC FIELD EFFECTS; MAGNETIC RESONANCE; MAGNETIC RESONANCE SPECTROSCOPY; MEDICAL PHYSICS; METABOLITES; NONINVASIVE MEDICAL PROCEDURES; SUPPORT VECTOR MACHINES;

BRAIN TUMOR CLASSIFICATIONS; DIAGNOSTIC APPLICATIONS; DIAGNOSTIC PROCESS; MAGNETIC FIELD HOMOGENEITY; MEASUREMENT NOISE; NON-INVASIVE DIAGNOSTICS; SENSOR CALIBRATION; SUPPORT VECTOR MACHINE CLASSIFICATION;

MEDICAL IMAGE PROCESSING;

EID: 77950126207     PISSN: 16800737     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-642-03882-2_362     Document Type: Conference Paper

References (11)

1. A.W. Simonetti, W.J. Melssen, M. Van der Graaf, G.J. Postma, A. Heerschap, and L.M.C. Buydens (2003) A Chemometric Approach for Brain Tumor Classification Using Magnetic Resonance Imaging and Spectroscopy, Analytical Chemistry, 75:5352-5361.
2. M.G. Kounelakis, M.E. Zervakis, M.E. Blazadonakis, G.J. Postma, L.M.C. Buydens, A. Heerschap, X. Kotsiakis Identification of significant Metabolic Markers from MRSI data for Brain Cancer Classification 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2008) Athens Greece, 8-10 Oct 2008
3. Jian, M. and S. Zengqi (2005). MRS Classification Based on Independent Component Analysis and Support Vector Machines. Proceedings of the Fifth International Conference on Hybrid Intelligent Systems, IEEE Computer Society.
4. Lukas, L., A. Devos, et al. (2004). "Brain tumor classification based on long echo proton MRS signals." Artificial intelligence in medicine 31(1): 73-89.
5. Vapnik, V. (1995) The Nature of Statistical Learning Theory. N.Y.: Springer.
6. Cristianini, N. and Shawe-Taylor, J. (2000) 'An introduction to Support Vector Machines and other kernel-based learning methods.' Cambridge ; New York: Cambridge University Press.
7. Duin, R. P. W. (2000) 'PRTools - A Matlab Toolbox for Pattern Recognition,' 4.1.3 (04.05.2008) ed, , www.prtools.org.
8. Van Gestel, T., J. A. K. Suykens, et al. (2002). "Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis." Neural Computation 14: 1115-1147.
9. A. Naressi, C. Couturier, J.M. Devos, M. Janssen, C. Mangeat, R. de Beer, D. Graveron-Demilly, MAGMA, 12, 141-152, 2001.
10. jMRUI project at http://www.mrui.uab.es/mrui/mruiHomePage.html
11. R. Areste, R., Yang, Y., Hsieh, J. (2006) "An Image Enhancement Procedure for 3D Visualization of Liver CT Data", Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation, pp.153-157.