• DocumentCode
    3013982
  • Title

    Detection and classification of MS using magnetisation transfer ratio images

  • Author

    Dehmeshki, J. ; Ruto, A.C. ; Parker, G.J.M. ; Arridge, S. ; Miller, D.H. ; Tofts, P.S.

  • Author_Institution
    Dept. of Clinical Neurol., Univ. Coll. London, UK
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    127
  • Lastpage
    132
  • Abstract
    Principal component (PCA) and multiple discriminant analysis (MDA) are applied to magnetization transfer ratio (MTR) images in multiple sclerosis (MS). PCA and MDA are used to characterise subtle diffuse changes in MS. PCA is applied to MTR histograms to identify regions of significant variation. These areas are indicated as possible lesion areas. We compare two classifiers to recognise differences between normal controls and different types of MS disease; a Bayesian classifier is trained in PC space, and the histogram space is transformed to the optimal discriminant space for a nearest neighbor classifier
  • Keywords
    Bayes methods; biomagnetism; diseases; image classification; magnetisation; medical image processing; principal component analysis; Bayesian classifier; MDA; MS; MTR images; PCA; diffuse changes; histogram space; lesion areas; magnetisation transfer ratio images; multiple discriminant analysis; multiple sclerosis; nearest neighbor classifier; optimal discriminant space; principal component analysis; Bayesian methods; Diseases; Histograms; Image analysis; Lesions; Magnetic analysis; Magnetization; Multiple sclerosis; Optimal control; Principal component analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing and Analysis, 2000. IWISPA 2000. Proceedings of the First International Workshop on
  • Conference_Location
    Pula
  • Print_ISBN
    953-96769-2-4
  • Type

    conf

  • DOI
    10.1109/ISPA.2000.914902
  • Filename
    914902