liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Correlation controlled bilateral filtering of fMRI data
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2005 (English)In: Proceedings of the International Society for Magnetic Resonance in MEdicine Annual Meeting (ISMRM) 2005, 2005Conference paper, Published paper (Refereed)
Abstract [en]

A novel filtering method for analysis of fMRI data is presented. The method is based on weighted averaging of neighboring voxels whose time-series are, in a sense, similar. A comparison between the new method and other filtering strategies is also presented, and the novel method is shown to have superior ability to discriminate between active and inactive voxels.

Place, publisher, year, edition, pages
2005.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-28776Local ID: 13959OAI: oai:DiVA.org:liu-28776DiVA: diva2:249588
Conference
International Society for Magnetic Resonance in Medicine (ISMRM) 13th Scientific meeting and Exhibition, Miami, Florida, USA, 7-13 May 2005
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2014-10-08
In thesis
1. Adaptive spatial filtering of fMRI data
Open this publication in new window or tab >>Adaptive spatial filtering of fMRI data
2005 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Functional magnetic resonance imaging (tMRI) is a method for detecting brain regions that are activated when a certain task is carried out. The method is useful in planning of neurosurgical procedures, where knowledge of the exact locations of important functions is needed to avoid damage to these regions. It is also an important tool in neurological research, where it is used to investigate the function of the human brain.

To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing the task. The variations in image intensity over time is then compared to a model of the variations expected to be found in active parts of the brain. Locations where the intensity variations are similar to the model are considered to be activated by the task.

Since the images are very noisy, filtering is needed before the detection of activation. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive filtering of fMRI data. One of these is based on canonical correlation analysis (CCA), and is an extension of a previously proposed CCA-based method. As in the old method, CCA is used in each neighborhood to find a spatial fi lter that maximizes the correlation to the model of the intensity variation. A novel feature of the presented method is that it is rotationally invariant, i.e. that it is equally sensitivelo activated regions in different orientations.

The other method is based on bilateral filtering. This method creates spatial filters which averages pixels with similar intensity variation. Since these filters are not optimized to maximize the similarity to the model of activated signals, the risk of declaring inactive pixels as active is lower compared to CCA-based methods.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2005. 57 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1200
Series
LiU-TEK-LIC, 55
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-30122 (URN)15600 (Local ID)91-85457-43-4 (ISBN)15600 (Archive number)15600 (OAI)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-11-13

Open Access in DiVA

No full text

Authority records BETA

Rydell, JoakimBorga, MagnusKnutsson, Hans

Search in DiVA

By author/editor
Rydell, JoakimBorga, MagnusKnutsson, Hans
By organisation
Medical InformaticsCenter for Medical Image Science and Visualization (CMIV)The Institute of Technology
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 565 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf