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Träfflista för sökning "WFRF:(Borga Magnus) ;pers:(Knutsson Hans 1950)"

Sökning: WFRF:(Borga Magnus) > Knutsson Hans 1950

  • Resultat 1-10 av 38
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  • Friman, Ola, 1975-, et al. (författare)
  • A Correlation Framwork For Functional Mri Data Analysis.
  • 2001
  • Ingår i: Proceedings of SCIA 2001. Bergen,2001. - 8299594006 ; , s. 3-9
  • Konferensbidrag (refereegranskat)abstract
    • A correlation framework for detecting brain activity in functional MRI data is presented. In this framework, a novel method based on canonical correlation analysis follows as a natural extension of established analysis methods. The new method shows very good detection performance. This is demonstrated by localizing brain areas which control finger movements and areas which are involved in numerical mental calculation.
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  • Friman, Ola, 1975-, et al. (författare)
  • Detection of neural activity in functional MRI using canonical correlation analysis
  • 2001
  • Ingår i: Magnetic Resonance in Medicine. - 0740-3194 .- 1522-2594. ; 45:2, s. 323-330
  • Tidskriftsartikel (refereegranskat)abstract
    • A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated.
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  • Friman, Ola, 1975-, et al. (författare)
  • Exploratory fMRI analysis by autocorrelation maximization
  • 2002
  • Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 16:2, s. 454-464
  • Tidskriftsartikel (refereegranskat)abstract
    • A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.
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  • Friman, Ola, 1975-, et al. (författare)
  • Hierarchical temporal blind source separation of fMRI data
  • 2002
  • Ingår i: Proceedings of the ISMRM Annual Meeting (ISMRM'02).
  • Konferensbidrag (refereegranskat)abstract
    • Blind Source Separation (BSS) of fMRI data can be done both temporally and spatially. Temporal BSS of fMRI data has one fundamental problem not encountered in the spatial BSS approach. There are thousands of observed timecourses in an fMRI data set while the number of samples of each timecourse typically is less than two hundred. This re lation makes the problem of recovering the underlying temporal sources ill-posed. This contribution eliminates this problem by introducing a hierarchical approach for performing temporal BSS of fMRI data.
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  • Friman, Ola, 1975-, et al. (författare)
  • Recognizing emphysema - A neural network approach
  • 2002
  • Ingår i: Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:1). - : IEEE Computer Society. ; , s. 512-515
  • Konferensbidrag (refereegranskat)abstract
    • An accurate and fully automatic method for detecting and quantifying emphysema in CT-images is presented. The method is based on an image preprocessing step followed by a neural network classifier trained to separate true emphysema from artifacts. The proposed approach is shown to be superior to an established method when applied on real patient data.
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  • Resultat 1-10 av 38

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