liu.seSearch for publications in DiVA
Change search
Refine search result
1234567 1 - 50 of 755
CiteExportLink to result list
Permanent 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
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the 'Create feeds' function.
  • 1.
    Abrahamsson, Annelie
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Rzepecka, Anna
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundberg, Peter
    Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Dabrosin, Charlotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology.
    Dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment in vivo2016In: Oncoimmunology, ISSN 2162-4011, E-ISSN 2162-402X, Vol. 5, no 10, e1229723Article in journal (Refereed)
    Abstract [en]

    Inflammation is one of the hallmarks of carcinogenesis. High mammographic density has been associated with increased risk of breast cancer but the mechanisms behind are poorly understood. We evaluated whether breasts with different mammographic densities exhibited differences in the inflammatory microenvironment.Postmenopausal women attending the mammography-screening program were assessed having extreme dense, n = 20, or entirely fatty breasts (nondense), n = 19, on their regular mammograms. Thereafter, the women were invited for magnetic resonance imaging (MRI), microdialysis for the collection of extracellular molecules in situ and a core tissue biopsy for research purposes. On the MRI, lean tissue fraction (LTF) was calculated for a continuous measurement of breast density. LTF confirmed the selection from the mammograms and gave a continuous measurement of breast density. Microdialysis revealed significantly increased extracellular in vivo levels of IL-6, IL-8, vascular endothelial growth factor, and CCL5 in dense breast tissue as compared with nondense breasts. Moreover, the ratio IL-1Ra/IL-1 was decreased in dense breasts. No differences were found in levels of IL-1, IL-1Ra, CCL2, leptin, adiponectin, or leptin:adiponectin ratio between the two breast tissue types. Significant positive correlations between LTF and the pro-inflammatory cytokines as well as between the cytokines were detected. Stainings of the core biopsies exhibited increased levels of immune cells in dense breast tissue.Our data show that dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment and, if confirmed in a larger cohort, suggests novel targets for prevention therapies for women with dense breast tissue.

  • 2.
    Adelöf, Anna
    et al.
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Lindberg, Christina
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Barlow, Lotti
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Gerdin, Ulla
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Bränd Persson, Kristina
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Ericsson, Erika
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Testi, Stefano
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Nyström, Mikael
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Förvaltning av SNOMED CT som en del i det nationella fackspråket för vård och omsorg2011Report (Other (popular science, discussion, etc.))
    Abstract [sv]

    Förvaltningsrapporten fokuserar på Snomed CT, eftersom det redan i dag finns rutiner för förvaltningar av termbanken och nationella hälsorelaterade klassifikationer. Ett särskilt utvecklingsarbete kommer att krävas för dessa delar.

    Rapporten tar upp syfte och mål med förvaltningen. Utöver det redogör rapporten för vilka konkreta ansvarsområden som ingår i förvaltningen av Snomed CT. Målet för förvaltningen är att Socialstyrelsen regelbundet ska kunna tillhandahålla en kontrollerad och uppdaterad release av Snomed CT. Det skulle möjliggöra användning inom vård och omsorg. Rapporten tar även upp behovet av kompetens, utbildning och finansiella resurser.

  • 3.
    Adolfsson, Karin
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Visual Evaluation of 3D Image Enhancement2006Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.

    The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.

    The results of this work are;

    1. a parameter setting tool that makes the initial parameter setting much easier and

    2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.

  • 4.
    Andersen, Per Øivin
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Mobile-supported life charting for bipolar patients - user requirements study2013In: MEDINFO 2013: proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Christoph Ulrich Lehmann, Elske Ammenwerth, Christian Nøhr, IOS Press, 2013, 1111- p.Conference paper (Other academic)
    Abstract [en]

    It is assumed that bipolar disorder patients can benefit from monitoring their mood, sleep, medicine intake and behavior which could be both done by patients themselves and in cooperation with health care professionals. This study aims at understanding what is required from a computerized system, as seen from the view of therapists and the patients, and how the newer mobile technologies (smart phones and tablets) can be utilized to support development of such a system. The study focuses on several existing solutions available either freely or on the market. Then these solutions are evaluated by both patients and medical professionals as a part of the system requirements study to be used in a new system development that will utilize mobile technologies to support the performance and patient outcomes.

  • 5.
    Andersson, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Automatic vertebrae detection and labeling in sagittal magnetic resonance images2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Radiologists are often plagued by limited time for completing their work, with an ever increasing workload. A picture archiving and communication system (PACS) is a platform for daily image reviewing that improves their work environment, and on that platform for example spinal MR images can be reviewed. When reviewing spinal images a radiologist wants vertebrae labels, and in Sectra's PACS platform there is a good opportunity for implementing an automatic method for spinal labeling. In this thesis a method for performing automatic spinal labeling, called a vertebrae classifier, is presented. This method should remove the need for radiologists to perform manual spine labeling, and could be implemented in Sectra's PACS software to improve radiologists overall work experience.Spine labeling is the process of marking vertebrae centres with a name on a spinal image. The method proposed in this thesis for performing that process was developed using a machine learning approach for vertebrae detection in sagittal MR images. The developed classifier works for both the lumbar and the cervical spine, but it is optimized for the lumbar spine. During the development three different methods for the purpose of vertebrae detection were evaluated. Detection is done on multiple sagittal slices. The output from the detection is then labeled using a pictorial structure based algorithm which uses a trained model of the spine to correctly assess correct labeling.

    The suggested method achieves 99.6% recall and 99.9% precision for the lumbar spine. The cervical spine achieves slightly worse performance, with 98.1% for both recall and precision. This result was achieved by training the proposed method on 43 images and validated with 89 images for the lumbar spine. The cervical spine was validated using 26 images. These results are promising, especially for the lumbar spine. However, further evaluation is needed to test the method in a clinical setting.

  • 6.
    Andersson, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wintersteller, Robert
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Utveckling av analysmodul till Zenicor Medical Systems EKG-system2005Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The mainpart of this thesis is about the developing of a new analysis tool to be used in Zenicor Medical Systems AB ECG-system. The primary task of the system is to simplify the ECG survey for patients who suffers from different kinds of arrythmias, for example heart fibrillation. With this system is it possible for the patients to do their ECG survey by them self at home and then send the signal with their telephone or mobilphone to a server. The equipment used to do the survey is not bigger than you can have it in a pocket and this results in a bigger flexibility for the patient. A doctor can connect to the server and analys the ECG-curve and follow up the patients condition.

  • 7.
    Andersson, Kenneth
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Quality and motion estimation for image sequence coding2002Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Since the advent of television obtaining high perceived quality using a limited bandwidth has been an important issue. This thesis proposes methods inspired and based on the human visual system for exploitation of temporal and perceptual redundancy in image sequences to achieve lower coding rate and/ or higher visual quality. Particularly relevant in the present context are the indications that the visual cortex contains cells that are selective in orientation and frequency but invariant to the phase of the stimuli. For this reason and for computational efficiency, a spatial quadrature filter bank is generated initially using combinations of simple sequential 1D filter kernels.

    The temporal redtmdancy is reduced using motion compensated prediction. Motion compensation predicts the frame to be transmitted by moving the previous decoded frame according to its motion, so called backward coding. This means that no information about motion needs to be transmitted to the decoder. The irregular sampling of the prediction, due to motion estimates with sub-pixel accuracy, is dealt with using a new method, continuous normalized convolution. Image values on an interlaced sampling grid are estimated from the irregularly sampled predictions using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the integration filter. The motion is estimated from products of quadrature filter bank responses, generating phasebased channels tuned for velocity. This new approach includes learning parameters for motion estimation and introduces multiple hierarchical motion estimation toachieve estimates with high spatial resolution.

    Using the quadrature filter bank a new quality estimator, based on a human visual system model, is generated. The quality estimator can be used to reduce the perceptual redundancy of non-visible errors and is intended to improve coding performance in such regions.

  • 8.
    Andersson, Kenneth
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Johansson, Peter
    ISY LiTH.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Motion compensation using backward prediction and prediction refinement2003In: Signal Processing: Image Communication, ISSN 0923-5965, Vol. 18, no 5, 381-400 p.Article in journal (Refereed)
    Abstract [en]

    This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion estimation is based on previously decoded field-images. The motion is then temporally predicted and used for motion compensated prediction of the field-image to be coded. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the specific characteristics of the field-image to be coded, the initially predicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.

  • 9.
    Andersson, Kenneth
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A perception based velocity estimator and its use for motion compensated prediction2001In: SCIA 2001 Scandinavian Conference on Image Analysis,2001, 2001, 493-499 p.Conference paper (Refereed)
    Abstract [en]

    The use of temporal redundancy is of vital importance for a successful video coding algorithm. An effective approach is the hybrid video coder where motion estimation is used for prediction of the next image frame and code the prediction error, and the motion field. The standard method for motion estimation is block matching as in MPEG-2, typically resulting in block artifacts. In this paper a perception based velocity estimator and its use for pixel based motion compensated prediction of interlaced video is presented.

  • 10.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Johansson, Peter
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Backward-forward motion compensated prediction2002In: Proceedings of ACIVS 2002 (Advanced Concepts for Intelligent Vision Systems), Ghent, Belgium, September 9-11, 2002, 2002, 260-267 p.Conference paper (Refereed)
    Abstract [en]

    This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion is estimated using previously decoded field-images. An initial motion compensated prediction is produced using the assumption that the motion is predictable in time. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the specific characteristics of the field-image to be coded, the initially predicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.

  • 11.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Continuous normalized convolution2002In: Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on  (Volume:1), IEEE , 2002, 725-728 p.Conference paper (Refereed)
    Abstract [en]

    The problem of signal estimation for sparsely and irregularly sampled signals is dealt with using continuous normalized convolution. Image values on real-valued positions are estimated using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the used discrete filters. The result of the approach is that an output sample close to signals with high certainty is interpolated using a small neighbourhood. An output sample close to signals with low certainty is spatially predicted from signals in a large neighbourhood.

  • 12.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Multiple hierarchical motion estimation2002In: Signal Processing, Pattern Recognition, and Applications - 2002 / [ed] M.H. Hamza, ACTA Press, 2002, 80- p.Conference paper (Refereed)
    Abstract [en]

    This paper introduce multiple hierarchical motion estimation to achieve motion estimates with high spatial resolution. The approach is based on phase-based motion estimation. Results show that the algorithm deal with the smooth motion field of hierarchical motion estimation while keeping the advantages of such an approach.

  • 13.
    Andersson, Kenneth
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Laboratory of Mathematics in Imaging, Harvard Medical School, Brigham and Women's Hospital, Boston, USA.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Prediction from off-grid samples using continuous normalized convolution2007In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 87, no 3, 353-365 p.Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel method for performing fast estimation of data samples on a desired output grid from samples on an irregularly sampled grid. The output signal is estimated using integration of signals over a neighbourhood employing a local model of the signal using discrete filters. The strength of the method is demonstrated in motion compensation examples by comparing to traditional techniques.

  • 14.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Global Search Strategies for Solving Multilinear Least-squares Problems2011Report (Other academic)
    Abstract [en]

    The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinearoperator is used in place of a matrix-vector product. The MLLS istypically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows formoving from one local minimizer to a better one. The efficiencyof this strategy isillustrated by results of numerical experiments performed forsome problems related to the design of filter networks.

  • 15.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, The Institute of Technology.
    Global search strategies for solving multilinear least-squares problems2012In: Sultan Qaboos University Journal for Science, ISSN 1027-524X, Vol. 17, no 1, 12-21 p.Article in journal (Refereed)
    Abstract [en]

    The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows for moving from one local minimizer to a better one. The efficiency of this strategy is illustrated by results of numerical experiments performed for some problems related to the design of filter networks.

  • 16.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Sparsity Optimization in Design of Multidimensional Filter Networks2015In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 16, no 2, 259-277 p.Article in journal (Refereed)
    Abstract [en]

    Filter networks are used as a powerful tool used for reducing the image processing time and maintaining high image quality.They are composed of sparse sub-filters whose high sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. Even when disregarding the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers, each of which is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.

    An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.

  • 17.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Sparsity Optimization in Design of Multidimensional Filter Networks2013Report (Other academic)
    Abstract [en]

    Filter networks is a powerful tool used for reducing the image processing time, while maintaining its reasonably high quality.They are composed of sparse sub-filters whose low sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. If to disregard the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers. Each of the local minimizers is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.

  • 18.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Adaptive Spatio-temporal Filtering of 4D CT-Heart2013In: Image Analyses: Image Processing, Computer Vision, Pattern Recognition, and Graphics / [ed] Joni-Kristian Kämäräinen, Markus Koskela, Berlin Heidelberg: Springer, 2013, 246-255 p.Conference paper (Refereed)
    Abstract [en]

    The aim of this project is to keep the x-ray exposure of the patient as low as reasonably achievable while improving the diagnostic image quality for the radiologist. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes.

    The proposed prototype system uses a novel filter set having directional filter responses being monomials. The monomial filter concept is used both for estimation of local structure and for the anisotropic adaptive filtering. Initial tests on clinical 4D CT-heart data with ECG-gated exposure has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. Another promising feature is that the reconstruction induced streak artifacts which generally occur in low dose CT are remarkably reduced in 4D.

  • 19.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Transformation of local spatio-temporal structure tensor fields2003In: Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on  (Volume:3 ), IEEE , 2003, 285-288 p.Conference paper (Refereed)
    Abstract [en]

    Tensors and tensor fields are commonly used in multidimensional signal processing to represent the local structure of the signal. This paper focuses on the case where the sampling on the original signal is anisotropic, e.g when the resolution of the multidimensional image varies depending on the direction which is common e.g. in medical imaging devices. To obtain a geometrically correct description of the local structure there are mainly two possibilities. To resample the image prior to the computation of the local structure tensor field or to compute the tensor field on the original grid and transform the result to obtain a correct geometry of the local structure. This paper deals with the latter alternative and contains an in depth theoretical analysis establishing the appropriate rules for tensor transformations induced by changes in space-time geometry with emphasis on velocity and motion estimation.

  • 20.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Kronander, Torbjorn
    Velocity Adaptive Filtered Angiography1999Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    A method of imaging a blood vessel in a body using X-rays and an injectable contrast medium is described. The contrast medium is injected into the body, and signals constituted by an X-ray image sequence depicting X-ray attenuation values is recorded. The X-ray attenuated values in each spaced-time neighborhood are combined in a way that is dependent on the processed image sequence and separately established for each neighborhood, and separating, from background and vessel signals, flow signals having energy contributions mainly in an area of frequency domain bounded by surfaces corresponding to threshold velocities separately established for each neighborhood, which surfaces are shifted a specified amount along a temporal frequency axis.

  • 21.
    Andersson, Mats
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Sandborg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Farnebäck, Gunnar
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Hans, Knutsson
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Adaptiv filtering of 4D-heart CT for image denoising and patient safety2010Conference paper (Other academic)
    Abstract [en]

    The aim of this medical image science project is to increase patient safety in terms of improved image quality and reduced exposure to ionizing radiation in CT. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes. Four-dimensional (4D) medical image data are captured as a time sequence of image volumes. During 4D image acquisition, a 3D image of the patient is recorded at regular time intervals. The resulting data will consequently have three spatial dimensions and one temporal dimension. Increasing the dimensionality of the data impose a major increase the computational demands. The initial linear filtering which is the cornerstone in all adaptive image enhancement algorithms increase exponentially with the dimensionality. On the other hand the potential gain in Signal to Noise Ratio (SNR) also increase exponentially with the dimensionality. This means that the same gain in noise reduction that can be attained by performing the adaptive filtering in 3D as opposed to 2D can be expected to occur once more by moving from 3D to 4D. The initial tests on on both synthetic and clinical 4D images has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. When tuning the parameters for adaptive filtering is extremely important to attain maximal diagnostic value which not necessarily coincide with an an eye pleasing image for a layman. Although this application focus on CT the resulting adaptive filtering methods will be beneficial for a wide range of 3D/4D medical imaging modalities e.g. shorter acquisition time in MRI and improved elimination of noise in 3D or 4D ultrasound datasets.

  • 22.
    Andersson, Mats T.
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Controllable 3-D Filters for Low Level Computer Vision1993In: SCIA8: Tromso, 1993Conference paper (Refereed)
    Abstract [en]

    Three-dimensional data processing is becoming more and more common. Typical operations are for example estimation of optical flow in video sequences and orientation estimation in 3-D MR images. This paper proposes an efficient approach to robust low level feature extraction for 3-D image analysis. In contrast to many earlier algorithms the methods proposed in this paper support the use of relatively complex models at the initial processing steps. The aim of this approach is to provide the means to handle complex events at the initial processing steps and to enable reliable estimates in the presence of noise. A limited basis filter set is proposed which forms a basis on the unit sphere and is related to spherical harmonics. From these basis filters, different types of orientation selective filters are synthesized. An interpolation scheme that provides a rotation as well as a translation of the synthesized filter is presented. The purpose is to obtain a robust and invariant feature extraction at a manageable computational cost.

  • 23.
    Andersson, Mats T.
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Orientation Estimation in Ambiguous Neighbourhoods1991In: Proceedings of SCIA91: Aalborg, Denmark, 1991Conference paper (Refereed)
    Abstract [en]

    This paper describes a new algorithm for local orientation estimation. The proposed algorithm detects and separates interfering events in ambiguous neighbourhoods and produces robust estimates of the two most dominant events. A representation suitable for simultaneous representation of two orientations is introduced. The main purpose of this representation is to make averaging of outputs for neigbourhoods containing two orientations possible. The feature extraction is performed by a set of quadrature filters. A method to obtain a large set of quadrature filter responses from a limited basis filter set is introduced. The estimation of the neighbourhood and the separation of the present events are based upon the quadrature responses in terms of local magnitude and phase. The performance of the algorithm is demonstrated using test images.

  • 24.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Filter Networks1999In: Proceedings of Signal and Image Processing (SIP'99), Nassau, Bahamas: IASTED , 1999, 213-217 p.Conference paper (Refereed)
    Abstract [en]

    This paper presents a new and efficient approach for optimization and implementation of filter banks e.g. velocity channels, orientation channels and scale spaces. The multi layered structure of a filter network enable a powerful decomposition of complex filters into simple filter components and the intermediary results may contribute to several output nodes. Compared to a direct implementation a filter network uses only a fraction of the coefficients to provide the same result. The optimization procedure is recursive and all filters on each level are optimized simultaneously. The individual filters of the network, in general, contain very few non-zero coefficients, but there are are no restrictions on the spatial position of the coefficients, they may e.g. be concentrated on a line or be sparsely scattered. An efficient implementation of a quadrature filter hierarchy for generic purposes using sparse filter components is presented.

  • 25.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Läthén, Gunnar
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A Fast Optimization Method for Level Set Segmentation2009In: Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen, Springer Berlin/Heidelberg, 2009, 400-409 p.Conference paper (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

  • 26.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Modified Gradient Search for Level Set Based Image Segmentation2013In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, 621-630 p.Article in journal (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

  • 27.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Consistent intensity inhomogeneity correction in water–fat MRI2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 2, 468-476 p.Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS:

    CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION:

    CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.

  • 28.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA)2012In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012, 2012Conference paper (Other academic)
  • 29.
    Antonsson, Johan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Quality of life using profile in coronary artery bypass surgery patients1999In: AMIA99,1999, Philadelphia: Hanley & Belfus Inc , 1999, 1013- p.Conference paper (Refereed)
  • 30.
    Antonsson, Johan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Kircher, Albert
    Technical University Graz Austria.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Uppsala .
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Design of a clinical decision support system for assist support devices in thoracic surgery2000In: AMIA,2000, Philadelphia: Hanley & Belfus Inc, , 2000Conference paper (Refereed)
  • 31.
    Arkad, Kristina
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Using Arden Syntax in decision support systems with special refrence to database integration1995Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Two of the most important issues in l!leveloping data-driven medical decision support systems are the choice of knowledge representation and the methods and technique for integration with the patient database. The main purpose of the Arden Syntax for Medical Logic Modules, which is the method of choice in this thesis, is to create a standard representation for data-driven decision support systems and to facilitate sharing of medical knowledge among different institutions and clinics. The main feature of the syntax is that it is easily readable and writeable, thereby allowing maintenance and transparency. The knowledge within a domain is braken down inta a number of independent medical logic modules (MLMs), where each module together with its logic also holds a description of its evocation criteria in terms of events and conditions related to patient data.

    This thesis addresses same of the problems with integration of a decision support system (OSS) inside a specific software engineering environment, together with issues in relation to database query handling. An architecture of a OSS based on Arden Syntax and its integration in an object-oriented environment is presented. The basic components of the OSS are the knowledge base composed of MLMs and the patient database with developed couplings between the knowledge base and the patient database. Since the MLM format is neutral to the local environment such as patient databases and computer platforms, these methods and tools are needed when realizing an efficient run-time OSS.

  • 32.
    Arkad, Kristina
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Medical Logic Module (MLM) representation of knowledge in a ventilator treatment advisory system1991In: International Journal of Clinical Monitoring and Computing, ISSN 0167-9945, Vol. 8, 43-48 p.Article in journal (Refereed)
  • 33.
    Arkad, Kristina
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Jean, Francois-Christophe
    Medical Informatics Dept Broussais University hospital, Paris.
    Degoulet, Patrice
    Medical Informatics Dept Broussais University Hospital, Paris.
    Integration of data driven decision support into the HELIOS environment1994In: International journal of bio-medical computing, ISSN 0020-7101, Vol. 34, 195-205 p.Article in journal (Refereed)
  • 34.
    Arkad, Kristina
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Query-handling in MLM-based decision support systems1996In: Medical Informatics & the Internet in Medicine, Vol. 20, no 3, 229-240 p.Article in journal (Refereed)
  • 35.
    Ask, Per
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Hägglund, Sture
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, ASLAB - Application Systems Laboratory.
    Olsson, J.
    Pettersson, N-E
    Sjöqvist, Bengt Arne
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    3G-satsning och 'pensionärsdatorer' kan lösa hälso- och sjukvårdens problem2003In: Läkartidningen, ISSN 0023-7205, Vol. 100, 1257-1258 p.Article in journal (Other academic)
  • 36.
    Ask, Per
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Hägglund, Sture
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Olsson, Jan
    Linköping University, Department of Management and Engineering. Linköping University, Faculty of Arts and Sciences.
    Pettersson, Nils-Erik
    Sjöqvist, Bengt-Arne
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    36-nätet och "pensionärsdatorer" kan bidra till att lösa sjukvårdens problem2003In: Läkartidningen, ISSN 0023-7205, Vol. 100, no 14, 1257-1258 p.Article in journal (Refereed)
  • 37.
    Aspevall, Olle
    et al.
    Karolinska Inst Stockholm.
    Forsum, Urban
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Clinical Microbiology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Microbiology.
    Karlsson, Daniel
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Preiminary report: Concepts and terms used to describe urinary tract infection in primary health care and in the clinical microbiology laboratory1999In: Medical Informatics Europe99,1999, Amsterdam: IOS Press , 1999, 899- p.Conference paper (Refereed)
  • 38.
    Aspevall, Olle
    et al.
    Karolinska institutet Stockholm.
    Karlsson, Daniel
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Forsum, Urban
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Clinical Microbiology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Microbiology.
    Building a concept system to structure the contents of a decision support system - a grounded theory study of concepts in the knowledge domain of urinary tract infection2001In: Medical Informatics & the Internet in Medicine, Vol. 26, no 2, 115-129 p.Article in journal (Refereed)
  • 39.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Case Based Reasoningin Support of the LVAD Surgical Treatment2013In: Medicinteknikdagarna 2013, Electronic Proceedings, 2013Conference paper (Refereed)
  • 40.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knowledge discovery for advanced clinical data management and analysis1999In: Medical Informatics Europe 99,1999, Amsterdam: IOS Press , 1999, 409- p.Conference paper (Refereed)
  • 41.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Medical knowledge extraction: application of data analysis methods to support clinical decisions1993Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In building computer based clinical decision support extensive data analysis is sought to acquire all the medical knowledge needed to formulate the decision rules.

    This study explores, compares and discusses several approaches to knowledge extraction from medical data. Statistical methods (univariate, multivariate), probabilistic artificial intelligence approaches (inductive learning procedures, neural networks) and the rough sets were used for this purpose. The methods were applied in two clinical sets of data with well defined patients groups.

    The aim of the study was then to use different data analytical methods and extract knowledge, both of semantic and classification nature, enabling to differentiate among patients, observations and disease groups, what in turn was aimed to support clinical decisions.

    Semantic analysis was performed in two ways. In prior analysis subgroups or patterns were formed based on the distance within the data, while in posterior semantic analysis 'types' of observation falling into various groups and their measured values were explored.

    To study further discrimination, two empirical systems, based on principles of learning from examples, i.e. based on Quintan's ID3 algorithm (the AssPro system) and CART (Classification and Regression Trees), were compared. The knowledge representation in both systems is tree structured, thus the comparison is made according to the complexity, accuracy and structure of their optimal decision trees. The inductive learning system was additionaly compared and evaluated in relation to the location model of discriminant analysis, the linear Ficherian discrimination and the rough sets.

    All methods used were analysed and compared for their theoretical and applicative performances, and in some cases they were assessed medical appropriateness. By using them for the extensive knowledge extraction, it was possible to give a strong methodological basis for design of clinical decision support systems specific for the problem and the medical environments considered.

  • 42.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Medical knowledge extraction. Applications of data analysis methods1992Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis we explore and discuss some important methods for knowledge extraction from meclical data. This is done in relation to, and for the purpose of design and development of decision support systems, which could be population specific.

    To test data and extract knowledge, we use univariate and multivariate statistical methods, the rough sets theory and probabilistic artificial intelligence approaches. These methods are used to estimate characteristics of patient groups, disease profiles and other features relevant for medical problems. In particular, we apply them to clifferentiate among patient groups, develop patient models and derive decision rules. Our experience refers to two medical domains (patients with diagnosed and non-diagnosed, but suspected liver disease and patients with duodenal ulcer surgery).

    Extracted knowledge can be used both in clinical practice and health care programs, as well as in computer based decision support systems to adjust them to various clinical environments.

  • 43.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Site specific outcomes analysis: includingknowledge from a limited set of the cardiac assist support data1999In: Medical Informatics Europe99,1999, Amsterdam: IOSPres , 1999, 987- p.Conference paper (Refereed)
  • 44.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Mathiesen, Ulrik
    Oskarshamns sjukhus .
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Franzén, Lennart
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Pathology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Pathology and Clinical Genetics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Machine learning to support diagnostics in the domain of asymptomatic liver disease1995In: MEDINFO95,1995, Edmonton: HC & CC , 1995, 809- p.Conference paper (Refereed)
  • 45.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Peeker, Martin
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Storm, Marcus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Thoraxkirurgi Uppsala.
    Casimir Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Case-based reasoning in a web-based clinical decision support system for thoracic surgery2002In: Am Medic Inform Ass Annual Symposium,2002, 2002, 968-968 p.Conference paper (Refereed)
  • 46.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Hedin, Kristina
    Linköping University, Department of Molecular and Clinical Medicine.
    Mathiesen, Ulrik
    Oskarshamns sjukhus .
    Franzén, Lennart
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Pathology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Pathology and Clinical Genetics.
    Frydén, Aril
    Linköping University, Department of Molecular and Clinical Medicine.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Decision support for monitoring of chronic Hepatitis C: can blood laboratory tests help?1996In: Medical Informatics Europe 96,1996, Amsterdam: IOS Press , 1996, 551- p.Conference paper (Refereed)
  • 47.
    Babic, Ankica
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Hiis Bergh, Fredrik
    Bjorgvin DPS, Helse Bergen HF, Norway.
    Rose Mari, Eikås
    Section for e-health, Helse Bergen, Norway.
    Grete, Mongstad
    National Association for the families of mentally ill, Bergen, Norway.
    Soerheim, Helen
    University of Bergen, Norway.
    Digi-Dag: Digital Diary for Users with Psychological  Disorders2013Conference paper (Other academic)
  • 48.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Koele, Werner
    Inst Biomed Engineering, Graz University Österike.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Lönn, Urban
    Dept of Cardio-Thoracic Surgery, Uppsala Universiet.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Help and advisory system in a Web-based system for data mining2001In: AMIA 2001,2001, Washington: Hanley&Belfus , 2001, 856- p.Conference paper (Refereed)
  • 49.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Krusinska, Ewa
    University of Wrocslaw .
    Koren, Iztok
    Faculty of Electrical and Computer Engineering Ljubljana.
    Gyergyek, Ludvik
    Faculty of Electrical and Computer Engineering Ljubljana.
    Semantic modelling of biomedical data1991In: International Symposium on Biomedical Engineering,1991, 1991, 282- p.Conference paper (Refereed)
  • 50.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Krusinska, Ewa
    IMT LiU.
    Strömberg, Jan-Erik
    Dept Electrical Engineering LiU.
    Extraction of diagnostic rules using recursive partitioning systems: A comparision of two approaches1992In: Artificial Intelligence in Medicine, ISSN 0933-3657, Vol. 4, 373-387 p.Article in journal (Refereed)
1234567 1 - 50 of 755
CiteExportLink to result list
Permanent 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