Segmentation of MR brain images through discriminant analysis (Articolo in rivista)

Type
Label
  • Segmentation of MR brain images through discriminant analysis (Articolo in rivista) (literal)
Anno
  • 2003-01-01T00:00:00+01:00 (literal)
Alternative label
  • Amato U.(*), Larobina M.(+), Antoniadis A.(**), Alfano B.(+) (2003)
    Segmentation of MR brain images through discriminant analysis
    in Journal of neuroscience methods
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Amato U.(*), Larobina M.(+), Antoniadis A.(**), Alfano B.(+) (literal)
Pagina inizio
  • 65 (literal)
Pagina fine
  • 74 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 131 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Article (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (*)Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR - Sezione di Napoli; (**)Laboratoire de Modelisation et Calcul IMAG, Grenoble (France); (+) Istituto di Biostrutture e Bioimmagini CNR, Napoli (literal)
Titolo
  • Segmentation of MR brain images through discriminant analysis (literal)
Abstract
  • Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T1w, T2w, PDw) the proposed family of statistical methods is based on: i) a transform of the images into components that are statistically independent from each other; ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; iii) a classic Bayes 0-1 classification rule. Experiments based on a computer built brain phantom (brainweb) and eight real patient data set are shown. A comparison with parametric discriminant analysis is also reported. The capability of nonparametric discriminant analysis in improving brain tissue classification of parametric methods is demonstrated. Finally, an assessment of the role of multispectrality in classifying brain tissues is discussed. (literal)
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