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Smoothing FMRI Data Using an Adaptive Wiener Filter

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor International Joint Conference on Computational Intelligence (4rt : 2012 : Barcelona, Catalunya)
dc.contributor IJCCI 2012
dc.contributor.author Bartés i Serrallonga, Manel
dc.contributor.author Serra Grabulosa, Josep M.
dc.contributor.author Adan, Ana
dc.contributor.author Falcón, Carles
dc.contributor.author Bargalló, Núria
dc.contributor.author Solé-Casals, Jordi
dc.date.accessioned 2015-01-26T12:15:04Z
dc.date.available 2015-01-26T12:15:04Z
dc.date.created 2015
dc.date.issued 2015
dc.identifier.isbn 978-3-319-11270-1
dc.identifier.issn 1860-949X
dc.identifier.uri http://hdl.handle.net/10854/3851
dc.description.abstract The analysis of fMRI allows mapping the brain and identifying brain regions activated by a particular task. Prior to the analysis, several steps are carried out to prepare the data. One of these is the spatial smoothing whose aim is to eliminate the noise which can cause errors in the analysis. The most common method to perform this is by using a Gaussian filter, in which the extent of smoothing is assumed to be equal across the image. As a result some regions may be under-smoothed, while others may be over-smoothed. Thus, we suggest smoothing the images adaptively using a Wiener filter which allows varying the extent of smoothing according to the changing characteristics of the image. Therefore, we compared the effects of the smoothing with a wiener filter and with a Gaussian Kernel. In general, the results obtained with the adaptive filter were better than those obtained with the Gaussian filter. en
dc.format application/pdf
dc.format.extent 12 p. ca_ES
dc.language.iso eng ca_ES
dc.rights (c) Springer (The original publication is available at www.springerlink.com)
dc.rights Tots els drets reservats ca_ES
dc.subject.other Separació (Tecnologia) ca_ES
dc.title Smoothing FMRI Data Using an Adaptive Wiener Filter en
dc.type info:eu-repo/semantics/bookPart ca_ES
dc.identifier.doi https://doi.org/10.1007/978-3-319-11271-8_21
dc.rights.accesRights info:eu-repo/semantics/closedAccess ca_ES

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