Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

Expert Systems with Applications

Volumn 39, Issue 5, 2012, Pages 5223-5232

  Arizmendi, Carlos ^a   Vellido, Alfredo ^a   Romero, Enrique ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Bayesian Neural Networks; Brain tumours; Magnetic Resonance Spectroscopy; Medical Decision Support

Indexed keywords

BAYESIAN NEURAL NETWORKS; BINARY CLASSIFICATION; BRAIN TUMOURS; CLASSIFICATION ACCURACY; CLINICAL TASKS; COMBINED METHOD; COMPUTER-BASED SYSTEM; DIMENSIONALITY REDUCTION; HIGH DIMENSIONALITY; HUMAN BRAIN; MEDICAL DECISION MAKING; MOVING WINDOW; NONINVASIVE TECHNIQUE; PRE-PROCESSING; ROBUST CLASSIFICATION; VARIANCE ANALYSIS;

BAYESIAN NETWORKS; BRAIN; DECISION SUPPORT SYSTEMS; DISCRETE WAVELET TRANSFORMS; FEATURE EXTRACTION; MAGNETIC RESONANCE; MAGNETIC RESONANCE SPECTROSCOPY; PRINCIPAL COMPONENT ANALYSIS; RADIOLOGY; TUMORS;

NEURAL NETWORKS;

EID: 84855889390     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.11.017     Document Type: Article

References (33)

1. C. Arizmendi, A. Vellido, and E. Romero Frequency selection for the diagnostic characterization of human brain tumours Frontiers in Artificial Intelligence and Applications 202 2009 391 398
2. 1H NMR spectra by optimization of signal acquisition and processing and by incorporation of prior knowledge into spectral fitting Magnetic Resonance in Medicine 15 2 1990 305 319
3. Devos, A. (2005). Quantification and classification of MRS data and applications to brain tumour recognition. Ph.D thesis, Katholieke University, Leuven, Belgium.
4. 1H MR spectra Journal of Magnetic Resonance 170 1 2004 164 175
5. D. Donoho De-noising by soft-thresholding IEEE Transactions on Information Theory 41 3 1995 613 627
6. Foresee, F.; Hagan, M. (1997). Gauss-Newton approximation to Bayesian regularization. In Proceedings of the IEEE international conference on neural networks (ICNN) pp. 1930-1935.
7. J. García-Gómez Multiproject-multicenter evaluation of automatic brain tumor classification by Magnetic Resonance Spectroscopy Magnetic Resonance Materials in Physics. Biology and Medicine (MAGMA) 22 1 2009 5 18
8. F. González-Navarro, L.A. Belanche-Muñoz, E. Romero, A. Vellido, M. Juli-Sapé, and C. Arús Feature and model selection with discriminatory visualization for diagnostic classification of brain tumors Neurocomputing 73 4-6 2010 622 632
9. Guo, D et al. (2000). A study of wavelet thresholding denoising. In Proceedings of the IEEE 5th international conference on signal processing (WCCC-ICSP) pp. 329-332.
10. Y. Huang, P.J.G. Lisboa, and W. El-Deredy Tumour grading from Magnetic Resonance Spectroscopy: a comparison of feature extraction with variable selection Statistics in Medicine 22 1 2003 147 164
11. Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI'2000) pp. 111-117.
12. M. Juli-Sapé, D. Acosta, M. Mier, C. Arús, D. Watson the INTERPRET Consortium A multi-centre, web-accessible and quality control checked database of in vivo MR spectra of brain tumour patients Magnetic Resonance Materials in Physics. Biology and Medicine (MAGMA) 19 1 2006 22 33
13. Ladroue, C.L.C. (2003). Pattern recognition techniques for the study of Magnetic Resonance Spectra of brain tumours, PhD thesis, St. George's Hospital Medical School, UK.
14. C.L.C. Ladroue, F. Howe, J. Griffiths, and R. Tate Independent component analysis for automated decomposition of in vivo magnetic resonance spectra Magnetic Resonance in Medicine 50 4 2003 697 703
15. R. Leardi Application of genetic algorithm-PLS for feature selection in spectral data sets Journal of Chemometrics 14 5-6 2000 643 655
16. 1H-MRS parameters Medical Engineering & Physics 24 3 2002 201 208
17. L. Lukas Brain tumor classification based on long-echo proton MRS signals Artificial Intelligence in Medicine 31 1 2004 73 89
18. Luts A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection Artificial Intelligence in Medicine 40 2 2007 87 102
19. D. MacKay The evidence framework applied to classification networks Neural Computation 4 5 1992 720 736
20. S. Mallat A wavelet tour of signal processing 1999 Academic Press San Diego (CA), USA
21. Minguillón, J.; Tate, A.R.; Arús, C.; Griffiths, J.R. (2002). Classifier combination for in vivo Magnetic Resonance Spectra of brain tumours. In Roli, F.; and Kittler, J. (Eds.), Multiple Classifier Systems. Lecture Notes in Computer Science, 2364 pp. 282-292.
22. M. Misiti, Y. Misiti, G. Oppenheim, and J-M. Poggi Wavelet toolbox for use with MatLab 2002 The Math Works, Inc
23. Nature (2009), 461 (7265), pp. 697-836.
24. O. Olarte, and D. Sierra Determinación de los parámetros asociados al filtro wavelet por umbralización aplicado a filtrado de interferencias electrocardiográficas UIS Ingenierías 6 2 2007 33 44
25. E. Rivas, J.C. Burgos, and J.C. García-Prada Condition assessment of power OLTC by vibration analysis using Wavelet Transform IEEE Transactions on Power Delivery 24 2 2009 687 694
26. 1 H-MRS brain tumour database. In Proceedings of the 26th annual meeting of the european society for magnetic resonance in medicine and biology (ESMRMB) p. 18.
27. 1 H-MRS brain tumor time-domain data Magnetic Resonance in Medicine 43 2000 649 656
28. 1H MRS brain tumor time-domain data Magnetic Resonance in Medicine 50 3 2003 623 626
29. B. Steward, and P. Kleihues World health organization 2003 World Cancer Report IARC Press
30. R. Tate, and Underwood Development of a decision support system for diagnosis and grading of brain tumours using in vivo Magnetic Resonance single voxel spectra NMR in Biomedicine 19 4 2006 411 434
31. A. Vellido, and P.J.G. Lisboa Investigating human cancer with Computational Intelligence techniques KES recent research results series 2009 Future Technology Press East Sussex, UK
32. A. Vellido, P.J.G. Lisboa, and D. Vicente Robust analysis of MRS brain tumour data using t-GTM Neurocomputing 69 7-9 2006 754 768
33. 1H-MRS brain tumour database Neurocomputing 72 13-15 2009 3085 3097