loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: M. Bartés-Serrallonga 1 ; J. M. Serra-Grabulosa 2 ; A. Adan 3 ; C. Falcón 4 ; N. Bargalló 5 and J. Solé-Casals 1

Affiliations: 1 University of Vic, Spain ; 2 Universitat de Barcelona and Institut d’ Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Spain ; 3 Universitat de Barcelona, Institute for Brain and Cognition and Behaviour (IR3C), Spain ; 4 Institut d’ Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and CIBER-BBN, Spain ; 5 Hospital Clínic de Barcelona, Spain

ISBN: 978-989-8565-33-4

ISSN: 2184-2825

Keyword(s): Adaptive Smoothing, fMRI, Wiener Filter, Smoothing, Gaussian Kernel, Noise.

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 th e 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. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.236.8.46

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bartés-Serrallonga, M.; M. Serra-Grabulosa, J.; Adan, A.; Falcón, C.; Bargalló, N. and Solé-Casals, J. (2012). Adaptive Smoothing Applied to fMRI Data.In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012) ISBN 978-989-8565-33-4, ISSN 2184-2825, pages 677-683. DOI: 10.5220/0004182306770683

@conference{sscn12,
author={M. Bartés{-}Serrallonga. and J. M. Serra{-}Grabulosa. and A. Adan. and C. Falcón. and N. Bargalló. and J. Solé{-}Casals.},
title={Adaptive Smoothing Applied to fMRI Data},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012)},
year={2012},
pages={677-683},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004182306770683},
isbn={978-989-8565-33-4},
}

TY - CONF

JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012)
TI - Adaptive Smoothing Applied to fMRI Data
SN - 978-989-8565-33-4
AU - Bartés-Serrallonga, M.
AU - M. Serra-Grabulosa, J.
AU - Adan, A.
AU - Falcón, C.
AU - Bargalló, N.
AU - Solé-Casals, J.
PY - 2012
SP - 677
EP - 683
DO - 10.5220/0004182306770683

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.