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Adaptive Smoothing Applied to fMRI Data

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor International Joint Conference on Computational Intelligence (4rt : 2012 : Barcelona, Catalunya)
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 2014-04-30T10:41:03Z
dc.date.available 2014-04-30T10:41:03Z
dc.date.created 2012
dc.date.issued 2012
dc.identifier.citation M. Bartés-Serrallonga, J. M. Serra-Grabulosa, A. Adan, C. Falcón, N. Bargalló and J. Solé- Casals, “Adaptive Smoothing Applied to fMRI Data”, Proceedings of IJCCI 2012. 4th International Joint Conference on Computational Intelligence. Barcelona (Spain). ISBN: 978-989-5868-33-4 ca_ES
dc.identifier.isbn 978-989-5868-33-4
dc.identifier.uri http://hdl.handle.net/10854/3016
dc.description IJCCI 2012 ca_ES
dc.description.abstract One problem of fMRI images is that they include some noise coming from many other sources like the heart beat, breathing and head motion artifacts. All these sources degrade the data and can cause wrong results in the statistical analysis. In order to reduce as much as possible the amount of noise and to improve signal detection, the fMRI data is spatially smoothed prior to the analysis. The most common and standardized method to do this task is by using a Gaussian filter. The principal problem of this method is that some regions may be under-smoothed, while others may be over-smoothed. This is caused by the fact that the extent of smoothing is chosen independently of the data and is assumed to be equal across the image. To avoid these problems, we suggest in our work to use an adaptive Wiener filter which smooths the images adaptively, performing a little smoothing where variance is large and more smoothing where the variance is small. In general, the results that we obtained with the adaptive filter are better than those obtained with the Gaussian kernel. In this paper we compare the effects of the smoothing with a Gaussian kernel and with an adaptive Wiener filter, in order to demonstrate the benefits of the proposed approach. ca_ES
dc.format application/pdf
dc.format.extent 7 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher SciTePress - Science and Technology Publications ca_ES
dc.rights (c) SciTePress - Science and Technology Publications
dc.rights Tots els drets reservats ca_ES
dc.subject.other Ressonància magnètica ca_ES
dc.title Adaptive Smoothing Applied to fMRI Data ca_ES
dc.type info:eu-repo/semantics/conferenceObject ca_ES
dc.rights.accesRights info:eu-repo/semantics/closedAccess ca_ES

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