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
Robust Correlation Analysis with an Application to Functional MRI
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
2008 (English)In: Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE, IEEE conference proceedings, 2008, 453-456 p.Conference paper, Published paper (Refereed)
Abstract [en]

Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are corrupted by local bursts of noise. This adversely affects the performance of signal recognition algorithms. This paper presents a novel correlation estimator, which is robust to locally corrupted signals. The estimator is generalized to multivariate correlation analysis (general linear model, GLM, and canonical correlation analysis, CCA). Synthetic functional MRI data is used to demonstrate the estimator, and its robustness is shown to increase the performance of signal detection.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2008. 453-456 p.
Series
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
Keyword [en]
biomedical MRI, correlation methods, medical signal processing, signal detection, canonical correlation analysis, correlation estimator, general linear model, locally corrupted signals, multivariate correlation analysis, robust correlation analysis, synthetic functional MRI data
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-12660DOI: 10.1109/ICASSP.2008.4517644ISI: 000257456700114ISBN: 978-1-4244-1483-3 (print)ISBN: e-978-1-4244-1484-0 OAI: oai:DiVA.org:liu-12660DiVA: diva2:16790
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008, 31 March-4 April 2008, Las Vegas, NV, USA
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Joakim Rydell, Magnus Borga and Hans Knutsson, Robust Correlation Analysis with an Application to Functional MRI, 2008, IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, Las Vegas, USA, 453-456. http://dx.doi.org/10.1109/ICASSP.2008.4517644

Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2015-10-09
In thesis
1. Advanced MRI Data Processing
Open this publication in new window or tab >>Advanced MRI Data Processing
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging (MRI) is a very versatile imaging modality which can be used to acquire several different types of images. Some examples include anatomical images, images showing local brain activation and images depicting different types of pathologies. Brain activation is detected by means of functional magnetic resonance imaging (fMRI). This is useful e.g. in planning of neurosurgical procedures and in neurological research. To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing a task. The variations in image intensity over time are then compared to a model of the variations expected to be found in active parts of the brain. Locations with high correlation between the intensity variations and the model are considered to be activated by the task.

Since the images are very noisy, spatial filtering is needed before the activation can be detected. 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 spatial filtering of fMRI data. One of these is a modification of a previously proposed method, which at each position maximizes the similarity between the filter response and the model. A novel feature of the presented method is rotational invariance, i.e. equal sensitivity to activated regions in different orientations. The other method is based on bilateral filtering. At each position, this method averages pixels which are located in the same type of brain tissue and have similar intensity variation over time.

A method for robust correlation estimation is also presented. This method automatically detects local bursts of noise in a signal and disregards the corresponding signal segments when the correlation is estimated. Hence, the correlation estimate is not affected by the noise bursts. This method is useful not only in analysis of fMRI data, but also in other applications where correlation is used to determine the similarity between signals.

Finally, a method for correcting artifacts in complex MR images is presented. Complex images are used e.g. in the Dixon technique for separate imaging of water and fat. The phase of these images is often affected by artifacts and therefore need correction before the actual water and fat images can be calculated. The presented method for phase correction is based on an image integration technique known as the inverse gradient. The method is shown to provide good results even when applied to images with severe artifacts.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2007. 91 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1140
Keyword
Magnetic resonance imaging (MRI), functional Magnetic Resonance Imaging (fMRI), spatial filtering, robust correlation estimation, correcting artifacts
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-10038 (URN)978-91-85895-59-5 (ISBN)
Public defence
2007-11-30, Linden, Hus 421, Campus US, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2014-10-08

Open Access in DiVA

fulltext(174 kB)858 downloads
File information
File name FULLTEXT01.pdfFile size 174 kBChecksum SHA-512
052ad42f08dc28a28a31c8827775e81f40eff04f6272f7d6aec3d4f1b3796d530a39e6dee891055efecb782fec15d0524ffab4a5e2069d7c24214f3812266d71
Type fulltextMimetype application/pdf

Other links

Publisher's full textLink to Ph.D. thesis

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
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 858 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 730 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