Title
Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images.
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 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.
Year
DOI
Venue
2014
10.1016/j.compmedimag.2014.03.003
Computerized Medical Imaging and Graphics
Keywords
DocType
Volume
Brain,Subcortical structures,Segmentation,Discriminant Analysis,Atlas-based,Denoising
Journal
38
Issue
ISSN
Citations 
5
0895-6111
1
PageRank 
References 
Authors
0.35
16
7
Name
Order
Citations
PageRank
Loredana Murino1202.91
Donatella Granata221.04
Maria Francesca Carfora3195.54
S. Easter Selvan4544.48
B Alfano5294.78
Umberto Amato6809.88
Michele Larobina7303.32