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Making decisions on brain tumor diagnosis by soft computing techniques

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Abstract

In this paper, a synergy of advanced signal processing and soft computing strategies is applied in order to identify different types of human brain tumors, as a help to confirm the histological diagnosis of experts and consequently to facilitate the decision about the correct treatment or the necessity of an operation. A computational tool has been developed that merges, on the one hand, wavelet transform to reduce the size of the biomedical spectra and to extract the main features, and on the other hand, Support Vector Machine and Neural Networks to classify them. The influence of some of the configuration parameters of each of those soft computing techniques on the clustering is analyzed. These two methods and another one based on medical knowledge are compared. The classification results obtained by these computational tools are promising specially taking into account that medical knowledge has not been considered and that the number of samples of each class is very low in some cases.

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References

  • Daubechies I (1992) Ten lectures on wavelets: CBMS lectures series. SIAM, Philadelphia

    MATH  Google Scholar 

  • Demuth H, Beale M (1998) Neural network toolbox user’s guide. The MathWorks Inc, USA

    Google Scholar 

  • Duda RO, Hart PE, Stork D (2001) Pattern classification. Wiley, New York

  • Farias G, Santos M (2005) Analysis of the wavelet transform parameters in images processing. LNCCS 2:51–54

    Google Scholar 

  • Farias G, Santos M, López V (2008) Brain tumour diagnosis with wavelets and support vector machines. Proceedings of the 3rd IEEE international conference on intelligent systems and knowledge engineering, pp 1453–1459

  • García-Martín ML, Hérigault G, Rémy CH, Farion R, Ballesteros P, Coles J, Cerdán S (2001) Mapping extracellular pH in rat brain gliomas “in vivo” by H magnetic resonance spectroscopic imaging: comparison with maps of metabolites. Cancer Res 61:6524–6531

    Google Scholar 

  • Hagberg G (1998) From magnetic resonance spectroscopy to classification tumors: a review of pattern recognition methods. NMR Biomed 11:148–156

    Article  Google Scholar 

  • Haykin S (1999) Neural networks: a comprehensive foundation, 2nd. edn. Prentice-Hall, Upper Saddle River, NJ

  • Hore P (1983) Solvent suppression in Fourier Transform nuclear magn. reson. J Magn Reson 55:283–300

    Google Scholar 

  • Howells SL, Maxwell R, Griffiths JR (1992) An investigation of tumor 1H NMR spectra by pattern recognition. NMR Biomed 5:59–64

    Article  Google Scholar 

  • Kim I, Watada J, Shigaki I (2008) Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decision. Soft Comput 12:191–199

    Google Scholar 

  • Kinoshita Y, Yokota A (1997) Absolute concentrations of metabolites in human brain tumours using in vitro proton magn. reson. spectroscopy. NMR Biomed 10:2–12

    Article  Google Scholar 

  • Laguna P, Moody GB, García J, Goldberger AL, Mark RG (1999) Analysis of the ST-T complex using the KL transform: adaptative monitoring and alternant detection. Med Biol Eng Comput 37(2):175–189

    Article  Google Scholar 

  • Mallat SG (2001) A wavelet tour of signal processing, 2nd edn. Academic Press, San Diego

    Google Scholar 

  • Martínez-Pérez I, Maxwell RJ, Howells SL, van der Bogaart A, Mazucco R, Griffiths JR, Arús C (1995) Pattern recognition analysis of 1H NMR spectra from human brain tumours biopsies. Proceedings Soc magnetic resonance. 3rd annual meeting Abstract P1709

  • MathWorks Inc (1989) MATLAB®, MA, USA

  • Maxwell RJ, Martínez-P. I, Cerdán S, Cabañas ME, Arús C, Moreno A, Capdevila A, Ferrer E, Bartomeus F, Aparicio A, Conesa G, Roda JM, Carceller F, Pascual JM, Howells SL, Mazzuco R, Griffiths J (1998) Pattern recognition analysis oh 1H NMR spectra from perchloric acid extracts of human brain tumor biopsies. Magn Reson Med 39:869–877

    Google Scholar 

  • Misiti M, Misiti Y, Oppenheim G, Poggi J-M (1997) Users Guide: Wavelet Toolbox for use with MATLAB. The MathWorks, Inc, Natick

  • Pascual J, Carceller F, Cerdán S, Roda JM (1998) Diagnóstico diferencial de tumores cerebrales “in vitro” por espectroscopia de resonancia magnética de protón. Método de los cocientes espectrales. Neurocirugía 9:4–10

    Google Scholar 

  • Peeling J, Sutherland G (1992) High-resolution 1H NMR spectroscopy studies of extracts of human cerebral neoplasm. Magn Reson Med 24:123–136

    Article  Google Scholar 

  • Rafiei D, Mendelzon A (1998) Efficient retrieval of similar time sequences using DFT. Proceedings of the 5th international conference on foundations of data organization, pp 249–257

  • Roda JM, Pascual JM, Carceller F, Gonzalez-Llanos F (2000) Nonhistological diagnosis of human cerebral tumors by 1H magnetic resonance spectroscopy and amino acid analysis. Clin Cancer Res 6:3983–3993

    Google Scholar 

  • Rojas R (1995) Neural networks: a systematic introduction. Springer, New York

  • Ruiz-Molina JC, Navarro-Moreno J, Oya A (2001) Signal detection using approximate Karhunen-Loève expansions. IEEE Trans Inf Theory 47(4):1672–1680

    Article  MATH  MathSciNet  Google Scholar 

  • Somorjai RL, Dolenko B, Nikulin AK, Pizzi N, Scarth G, Zhilkin P, Halliday W, Fewer D, Hill N, Ross I, West M, Smith ICP, Donnelly SM, Kuesel AC, Bière KM (1996) Classification of 1H MR spectra of human brain neoplasms: the influence of preprocessing and computerized consensus diagnosis on classification accuracy. J Magn Reson Imaging 6:437–444

    Article  Google Scholar 

  • Tate AR (1997) Statistical pattern recognition for the analysis of biomedical magnetic resonance spectra. J Magn Reson Anal 3:63–78

    Google Scholar 

  • Tate AR, Griffiths JR, Martínez PI, Moreno A, Barba I, Cabañas M, Watson D, Alonso J, Bartumeus F, Isamat F, Ferrer I, Vila F, Ferrer E, Capdevilla A, Arús C (1998) Towards a method for automated classification of 1H MRS spectra from brain tumours. NMR Biomed 11:177–191

    Article  Google Scholar 

  • Therrien CW (1992) Discrete random signals and statistical signal processing. Prentice-Hall, Upper Saddle River, NJ

  • Unser M, Aldroubi A (1995) A review of wavelets in biomedical applications. Proc IEEE 48:626–638

    Google Scholar 

  • Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New York

  • Wen X, Zhang H, Xu X, Quan J (2009) A new watermarking approach based on probabilistic neural network in wavelet domain. Soft Comput 13:355–360

    Article  Google Scholar 

  • Yin P, Sun F, Wang C, Liu H (2008) An adaptive feature fusion framework for multi-class classification based on SVM. Soft Comput 12:685–691

    Article  Google Scholar 

Download references

Acknowledgments

We appreciate the collaboration of the members of the Neurosurgery Service at University Hospital of La Paz (Madrid, Spain) and the Biomedical Research Institute (CSIC), who have provided us with the signals and data used in this work.

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Authors and Affiliations

  1. Dpto. Informática y Automática, Escuela Superior de Ingeniería Informática, UNED, 28040, Madrid, Spain

    G. Farias

  2. Dpto. Arquitectura de Computadores y Automática, Facultad Informática, UCM, 28040, Madrid, Spain

    M. Santos & V. López

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  1. G. Farias
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  2. M. Santos
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  3. V. López
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Correspondence to V. López.

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Farias, G., Santos, M. & López, V. Making decisions on brain tumor diagnosis by soft computing techniques. Soft Comput 14, 1287–1296 (2010). https://doi.org/10.1007/s00500-009-0495-0

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