Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images.
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 neighboring 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.
Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Napoli, Italy. Electronic address: l.murino@na.iac.cnr.it.
Loredana
L
Murino
Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Napoli, Italy.
Donatella
D
Granata
Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Napoli, Italy.
Maria Francesca
MF
Carfora
Université catholique de Louvain, Department of Mathematical Engineering, Louvain-la-Neuve, Belgium.
S Easter
SE
Selvan
Istituto di Biostrutture e Bioimmagini CNR, Napoli, Italy.
Bruno
B
Alfano
Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Napoli, Italy.
Umberto
U
Amato
Istituto di Biostrutture e Bioimmagini CNR, Napoli, Italy.
Michele
M
Larobina
D001921:Brain / Q000033:anatomy & histology*; D016002:Discriminant Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D060388:Support Vector Machine
Atlas-based; Brain; Denoising; Discriminant Analysis; Segmentation; Subcortical structures