Paper Info
Title
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome.
Abstract
In this paper, we propose a new method to detect differences at the group level in brain images based on spatially regularized support vector machines (SVM). We propose to spatially regularize the SVM using a graph Laplacian. This provides a flexible approach to model different types of proximity between voxels. We propose a proximity graph which accounts for tissue types. An efficient computation of the Gram matrix is provided. Then, significant differences between two populations are detected using statistical tests on the outputs of the SVM. The method was first tested on synthetic examples. It was then applied to 72 stroke patients to detect brain areas associated with motor outcome at 90days, based on diffusion-weighted images acquired at the acute stage (median delay one day). The proposed method showed that poor motor outcome is associated to changes in the corticospinal bundle and white matter tracts originating from the premotor cortex. Standard mass univariate analyses failed to detect any difference on the same population.
Year
DOI
Venue
2011
10.1016/j.media.2011.05.007
Medical Image Analysis
Keywords
Field
DocType
SVM,Regularization,Group analysis,Stroke,DWI
Voxel,Laplacian matrix,Population,Pattern recognition,Premotor cortex,Support vector machine,Regularization (mathematics),Artificial intelligence,Gramian matrix,Mathematics,Statistical hypothesis testing
Journal
Volume
Issue
ISSN
15
5
1361-8415
Citations 
PageRank 
References 
19
1.02
31
Authors
8
Name
Order
Citations
PageRank
Rémi Cuingnet141519.36
Charlotte Rosso2241.82
Marie Chupin340222.10
Stéphane Lehéricy446726.36
Didier Dormont522418.59
Habib Benali683768.94
Yves Samson7241.82
Olivier Colliot874349.59