Brain tumour diagnosis with Wavelets and Support Vector Machines

In this paper, a synergy of signal processing techniques and intelligent strategies is applied in order to identify different types of human brain tumours, so that to help to confirm the histological diagnosis. The wavelet-SVM (support vector machine) classifier merges wavelet transform to reduce the size of the biomedical spectra and to extract the main features, with SVM to classify them. The influence of some of the configuration parameters of each of those techniques on the clustering is analysed. The classification results are promising specially taking into account that medical knowledge has not been considered.

[1]  J R Griffiths,et al.  Classification of tumour 1H NMR spectra by pattern recognition , 1992, NMR in biomedicine.

[2]  J. Roda,et al.  Diagnóstico diferencial de tumores cerebrales “in vitro” por espectroscopía de resonancia magnética de protón. Método de los cocientes espectrales , 1998 .

[3]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[4]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[5]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[6]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[7]  S Cerdán,et al.  Nonhistological diagnosis of human cerebral tumors by 1H magnetic resonance spectroscopy and amino acid analysis. , 2000, Clinical cancer research : an official journal of the American Association for Cancer Research.

[8]  D Fewer,et al.  Classification of 1H MR spectra of human brain neoplasms: The influence of preprocessing and computerized consensus diagnosis on classification accuracy , 1996, Journal of magnetic resonance imaging : JMRI.

[9]  Michael Unser,et al.  A review of wavelets in biomedical applications , 1996, Proc. IEEE.

[10]  A R Tate,et al.  Towards a method for automated classification of 1H MRS spectra from brain tumours , 1998, NMR in biomedicine.

[11]  P. J. Hore,et al.  Solvent suppression in Fourier transform nuclear magnetic resonance , 1983 .

[12]  J R Griffiths,et al.  Pattern recognition analysis of 1H NMR spectra from perchloric acid extracts of human brain tumor biopsies , 1998, Magnetic resonance in medicine.

[13]  G. Hagberg,et al.  From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods , 1998, NMR in biomedicine.

[14]  G. Sutherland,et al.  High‐Resolution 1H NMR spectroscopy studies of extracts of human cerebral neoplasms , 1992, Magnetic resonance in medicine.

[15]  Y. Kinoshita,et al.  Absolute concentrations of metabolites in human brain tumors using in vitro proton magnetic resonance spectroscopy , 1997, NMR in biomedicine.

[16]  Alberto O. Mendelzon,et al.  Efficient Retrieval of Similar Time Sequences Using DFT , 1998, FODO.

[17]  S Cerdán,et al.  Mapping extracellular pH in rat brain gliomas in vivo by 1H magnetic resonance spectroscopic imaging: comparison with maps of metabolites. , 2001, Cancer research.