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On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation.
Postma, Geert J; Luts, Jan; Idema, Albert J; Julià-Sapé, Margarida; Moreno-Torres, Angel; Gajewicz, Witek; Suykens, Johan A K; Heerschap, Arend; Van Huffel, Sabine; Buydens, Lutgarde M C.
Afiliação
  • Postma GJ; Institute for Molecules and Materials, Radboud University Nijmegen, Heijendaalseweg 135, 6525 AJ Nijmegen, The Netherlands. g.j.postma@science.ru.nl
Comput Biol Med ; 41(2): 87-97, 2011 Feb.
Article em En | MEDLINE | ID: mdl-21236418
ABSTRACT
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Neoplasias Encefálicas / Imageamento por Ressonância Magnética / Biologia Computacional Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2011 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Neoplasias Encefálicas / Imageamento por Ressonância Magnética / Biologia Computacional Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2011 Tipo de documento: Article