Document Detail


On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation.
MedLine Citation:
PMID:  21236418     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
In order to evaluate the relevance of magnetic resonance (MR) features selected by automatic feature selection techniques to build classifiers for differential diagnosis and tissue segmentation two data sets containing MR spectroscopy data from patients with brain tumours were investigated. The automatically selected features were evaluated using literature and clinical experience. It was observed that a significant part of the automatically selected features correspond to what is known from the literature and clinical experience. We conclude that automatic feature selection is a useful tool to obtain relevant and possibly interesting features, but evaluation of the obtained features remains necessary.
Authors:
Geert J Postma; Jan Luts; Albert J Idema; Margarida Julià-Sapé; Angel Moreno-Torres; Witek Gajewicz; Johan A K Suykens; Arend Heerschap; Sabine Van Huffel; Lutgarde M C Buydens
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Publication Detail:
Type:  Journal Article     Date:  2011-01-13
Journal Detail:
Title:  Computers in biology and medicine     Volume:  41     ISSN:  1879-0534     ISO Abbreviation:  Comput. Biol. Med.     Publication Date:  2011 Feb 
Date Detail:
Created Date:  2011-01-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  87-97     Citation Subset:  IM    
Copyright Information:
Copyright © 2010 Elsevier Ltd. All rights reserved.
Affiliation:
Institute for Molecules and Materials, Radboud University Nijmegen, Heijendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
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