• DocumentCode
    2721465
  • Title

    Does the combination of magnetic resonance imaging and spectroscopic imaging improve the classification of brain tumours?

  • Author

    Devos, A. ; Lukas, L. ; Simonetti, A.W. ; Suykens, J.A.K. ; Vanhamme, L. ; van der Graaf, M. ; Buydens, L.M.C. ; Heerschap, A. ; Van Huffel, S.

  • Author_Institution
    Dept. of Electr. Eng., Katholieke Univ., Leuven, Belgium
  • Volume
    1
  • fYear
    2004
  • fDate
    1-5 Sept. 2004
  • Firstpage
    407
  • Lastpage
    410
  • Abstract
    Magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI) play an important role in the noninvasive diagnosis of brain tumours. We investigate the use of both MRI and MRSI, separately and in combination with each other for classification of brain tissue types. Many clinically relevant classification problems are considered; for example healthy versus tumour tissues, low- versus high-grade tumours. Linear as well as nonlinear techniques are compared. The classification performance is evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). In general, all techniques achieve a high performance, except when using MRI alone. For example, for low- versus high-grade tumours, low- versus high-grade gliomas, gliomas versus meningiomas, respectively a test AUC higher than 0.91, 0.93 and 0.98 is reached, when both MRI and MRSI data are used.
  • Keywords
    biomedical MRI; brain; cancer; image classification; medical image processing; sensitivity analysis; tumours; brain tissue types; brain tumour classification; high-grade gliomas; high-grade tumours; low-grade gliomas; low-grade tumours; magnetic resonance imaging; magnetic resonance spectroscopic imaging; meningiomas; noninvasive diagnosis; receiver operating characteristic; Biomedical imaging; Biopsy; Brain; Linear discriminant analysis; Magnetic resonance; Magnetic resonance imaging; Noninvasive treatment; Spectroscopy; Testing; Tumors; Classification; Least Squares Support Vector Machine (LS-SVM); Linear Discriminant Analysis (LDA); Magnetic Resonance Imaging (MRI); Magnetic Resonance Spectroscopic Imaging (MRSI); brain tumours;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-8439-3
  • Type

    conf

  • DOI
    10.1109/IEMBS.2004.1403180
  • Filename
    1403180