Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images (Articolo in rivista) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1016/j.compmedimag.2014.03.003 (literal)

Alternative label

• Loredana Murino, Donatella Granata, Maria Francesca Carfora, S. Easter Selvan, Bruno Alfano, Umberto Amato, Michele Larobina (2014)
  Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images
  in Computerized medical imaging and graphics
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Loredana Murino, Donatella Granata, Maria Francesca Carfora, S. Easter Selvan, Bruno Alfano, Umberto Amato, Michele Larobina (literal)

Rivista

• Computerized medical imaging and graphics (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Istituto per le Applicazioni del Calcolo \"Mauro Picone\" CNR, Napoli, Italy; Istituto di Biostrutture e Bioimmagini CNR, Napoli, Italy; Université catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium (literal)

Titolo

• Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images (literal)

Abstract

• This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighbouring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images. (literal)

Prodotto di

• Institute of biostructure and bioimaging (IBB) (Istituto)
• Istituto per le applicazioni del calcolo \"Mauro Picone\" (IAC) (Istituto)

Autore CNR

• LOREDANA MURINO (Persona)
• MICHELE LAROBINA (Persona)
• UMBERTO AMATO (Unità di personale interno)
• MARIA FRANCESCA CARFORA (Persona)
• DONATELLA GRANATA (Unità di personale esterno)
• BRUNO ALFANO (Unità di personale interno)
• GENNARO ANGRISANO (Unità di personale interno)

Incoming links:

Prodotto

• Istituto per le applicazioni del calcolo \"Mauro Picone\" (IAC) (Istituto)
• Institute of biostructure and bioimaging (IBB) (Istituto)

Autore CNR di

• MARIA FRANCESCA CARFORA (Persona)
• BRUNO ALFANO (Unità di personale interno)
• UMBERTO AMATO (Unità di personale interno)
• GENNARO ANGRISANO (Unità di personale interno)
• MICHELE LAROBINA (Persona)
• LOREDANA MURINO (Persona)
• DONATELLA GRANATA (Unità di personale esterno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Computerized medical imaging and graphics (Rivista)