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Karlsson, Patrick
Alternative names
Publications (10 of 14) Show all publications
Cristea, A., Karlsson Edlund, P., Lindblad, J., Qaisar, R., Bengtsson, E. & Larsson, L. (2009). Effects of ageing and gender on the spatial organization of nuclei in single human skeletal muscle cells. Neuromuscular Disorders, 19, 605-606
Open this publication in new window or tab >>Effects of ageing and gender on the spatial organization of nuclei in single human skeletal muscle cells
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2009 (English)In: Neuromuscular Disorders, ISSN 0960-8966, E-ISSN 1873-2364, Vol. 19, p. 605-606Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-97429 (URN)10.1016/j.nmd.2009.06.196 (DOI)
Available from: 2008-08-29 Created: 2008-08-29 Last updated: 2018-08-24Bibliographically approved
Wählby, C., Karlsson, P., Henriksson, S., Larsson, C., Nilsson, M. & Bengtsson, E. (2008). Finding cells, finding molecules, finding patterns. International Journal of Signal and Imaging Systems Engineering, 1(1), 11-17
Open this publication in new window or tab >>Finding cells, finding molecules, finding patterns
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2008 (English)In: International Journal of Signal and Imaging Systems Engineering, ISSN 1748-0698, Vol. 1, no 1, p. 11-17Article in journal (Refereed) Published
Abstract [en]

Many modern molecular labelling techniques result in bright point signals. Signals from molecules that are detected directly inside a cell can be captured by fluorescence microscopy. Signals representing different types of molecules may be randomly distributed in the cells or show systematic patterns, indicating that the corresponding molecules have specific, non-random localisations and functions in the cell. Assessing this information requires high speed robust image segmentation followed by signal detection, and finally, pattern analysis. We present and discuss these types of methods and show an example of how the distribution of different variants of mitochondrial DNA can be analysed.

Keywords
mass data analysis, image analysis, cytometry, single molecule detection, padlock probes, pattern analysis
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-97427 (URN)10.1504/IJSISE.2008.017768 (DOI)
Available from: 2008-08-29 Created: 2008-08-29 Last updated: 2018-01-13Bibliographically approved
Karlsson Edlund, P. (2008). Introduction to the Mean-Shift Procedure: Filtering and Segmentation. Centre for Image Analysis, Uppsala University
Open this publication in new window or tab >>Introduction to the Mean-Shift Procedure: Filtering and Segmentation
2008 (English)Report (Other academic)
Place, publisher, year, edition, pages
Centre for Image Analysis, Uppsala University, 2008
Series
Internal Report ; 47
Identifiers
urn:nbn:se:uu:diva-97431 (URN)
Available from: 2008-08-29 Created: 2008-08-29 Last updated: 2010-03-29Bibliographically approved
Karlsson Edlund, P. & Lindblad, J. (2008). Non-uniform 3D distance transform for anisotropic signal correction in confocal image volumes of skeletal muscle cell nuclei. In: Proc. 5th International Symposium on Biomedical Imaging (pp. 1363-1366). Piscataway, NJ: IEEE
Open this publication in new window or tab >>Non-uniform 3D distance transform for anisotropic signal correction in confocal image volumes of skeletal muscle cell nuclei
2008 (English)In: Proc. 5th International Symposium on Biomedical Imaging, Piscataway, NJ: IEEE , 2008, p. 1363-1366Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2008
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-97428 (URN)10.1109/ISBI.2008.4541258 (DOI)978-1-4244-2002-5 (ISBN)
Available from: 2008-08-29 Created: 2008-08-29 Last updated: 2018-08-24Bibliographically approved
Karlsson, P., Lindblad, J., Bengtsson, E., Höglund, A.-S., Liu, J. & Larsson, L. (2007). Analysis of Skeletal Fibers in Three Dimensional Images. In: Medicinteknikdagarna 2007 (pp. 1).
Open this publication in new window or tab >>Analysis of Skeletal Fibers in Three Dimensional Images
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2007 (English)In: Medicinteknikdagarna 2007, 2007, p. 1-Conference paper, Published paper (Other (popular science, discussion, etc.))
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-12575 (URN)
Available from: 2008-01-10 Created: 2008-01-10 Last updated: 2018-08-24Bibliographically approved
Karlsson, P., Lindblad, J., Bengtsson, E., Höglund, A.-S., Liu, J. & Larsson, L. (2007). Analysis of Skeletal Fibers in Three Dimensional Images: Methodological considerations. In: XXXVIth European Muscle Conference of the European Society for Muscle Research: European Muscle Conference 2007 (pp. 130).
Open this publication in new window or tab >>Analysis of Skeletal Fibers in Three Dimensional Images: Methodological considerations
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2007 (English)In: XXXVIth European Muscle Conference of the European Society for Muscle Research: European Muscle Conference 2007, 2007, p. 130-Conference paper, Published paper (Other academic)
Abstract [en]

Knowledge of the detailed three dimensional organization of nuclei in skeletal muscle fibers is of fundamental importance for the understanding of the basic mechanisms involved in muscle wasting associated with for example neuromuscular disorders and aging. An ongoing interdisciplinary collaboration between the Centre for Image Analysis (CBA), and the Muscle Research Group (MRG), both at Uppsala University, addresses the issue of spatial distribution of myonuclei using confocal microscopic techniques together with advanced methods for computerized image analysis.

Performing quantitative analysis on true three dimensional volume images captured by confocal microscopy gives us the option to perform in-depth statistical analysis of the relationship between neighboring myonuclei. The three dimensional representation enables extraction of a number of features for individual myonuclei, e.g., size and shape of a nucleus, and the myonuclear domain (in which each myonucleus control the gene products). This project investigates data sets from single muscle fibers sampled from mouse, rat, pig, human, horse and rhino to determine the myonuclei arrangement between species with a 100,000 fold difference in body weight.

The appropriate image analysis tools needed for gaining the understanding of organization in three dimensional volume images are developed within the project to facilitate the analysis of similarities between species, and unique features within a species. The accumulated understanding of the spatial organization of myonuclei, and the effect of individual myonuclei size, will lead to an increased knowledge of basic mechanisms underlying muscle wasting in various neuromuscular disorders. This knowledge will hopefully lead to new therapeutic strategies that can be evaluated in experimental animal models prior to clinical testing trials in patients.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-12567 (URN)
Note
Abstract at p. 101.Available from: 2008-01-07 Created: 2008-01-07 Last updated: 2018-08-24Bibliographically approved
Höglund, A.-S., Liu, J., Karlsson, P., Lindblad, J., Borgefors, G., Bengtsson, E. & Larsson, L. (2007). The spatial distribution of nuclei in single skeletal muscle cells as visualised by 3-D images:: the differences in organisation between species and between healthy cells and cells affected by disease. In: Biophysical Journal: 637A-637A Suppl. S.
Open this publication in new window or tab >>The spatial distribution of nuclei in single skeletal muscle cells as visualised by 3-D images:: the differences in organisation between species and between healthy cells and cells affected by disease
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2007 (English)In: Biophysical Journal: 637A-637A Suppl. S, 2007Conference paper, Published paper (Other scientific)
Identifiers
urn:nbn:se:uu:diva-12445 (URN)0006-3495 (ISBN)
Available from: 2007-12-20 Created: 2007-12-20 Last updated: 2018-08-24
Wählby, C., Karlsson, P., Henriksson, S., Larsson, C., Nilsson, M. & Bengtsson, E. (2006). Finding cells, finding molecules, finding patterns. In: Advances in Data Mining: Workshop on Mass-Data Analysis of Images and Signals in Medicine, Biotechnology and Chemistry, MDA´2006, Leipzig/Germany (pp. 15-24).
Open this publication in new window or tab >>Finding cells, finding molecules, finding patterns
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2006 (English)In: Advances in Data Mining: Workshop on Mass-Data Analysis of Images and Signals in Medicine, Biotechnology and Chemistry, MDA´2006, Leipzig/Germany, 2006, p. 15-24Conference paper, Published paper (Refereed)
Abstract [en]

Many modern molecular labeling techniques result in bright point signals. Signals from molecules that are detected directly inside a cell can be captured by fluorescence microscopy. Signals representing different types of molecules may be randomly distributed in the cells or show systematic patterns indicating that the corresponding molecules have specific, non-random localizations and functions in the cell. Assessing this information requires high speed robust image segmentation followed by signal detection, and finally pattern analysis. We present and discuss this type of methods and show an example of how the distribution of different variants of mitochondrial DNA can be analyzed.

Keywords
mass data analysis; image analysis; cytometry; single molecule detection; padlock probes; pattern analysis.
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-26039 (URN)
Note
Accepted for publication in International Journal of Signal and Imaging Systems Engineering (IJSISE), 2006 (http://www.inderscience.com/browse/index.php?journalID=185)Available from: 2009-03-16 Created: 2009-03-06 Last updated: 2018-01-12Bibliographically approved
Degerman, J., Althoff, K., Thorlin, T., Wählby, C., Karlsson, P., Bengtsson, E. & Gustavsson, T. (2004). Modeling stem cell migration by Hidden Markov. In: Proceedings of the Swedish Symposium on Image Analysis, SSBA 2004 (pp. 122-125).
Open this publication in new window or tab >>Modeling stem cell migration by Hidden Markov
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2004 (English)In: Proceedings of the Swedish Symposium on Image Analysis, SSBA 2004, 2004, p. 122-125Conference paper, Published paper (Other scientific)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-67590 (URN)
Available from: 2005-05-23 Created: 2005-05-23 Last updated: 2018-01-10
Karlsson, P., Lindblad, J. & Wählby, C. (2004). Segmentation of point-like fluorescent markers. In: Proceedings: Symposium on Image Analysis. Paper presented at Swedish Society for Automated Image Analysis (SSBA) Symposium on Image Analysis, Uppsala, Sweden, March 2004. (pp. 146-149).
Open this publication in new window or tab >>Segmentation of point-like fluorescent markers
2004 (English)In: Proceedings: Symposium on Image Analysis, 2004, p. 146-149Conference paper, Published paper (Other academic)
Abstract [en]

We present a method for accurate segmentation of point like signals, from fluorescent markers in digital microscopic images with subcellular resolution. The method is able to segment and separate clustered signals, which facilitates accurate dot counting. The method performance is evaluated using synthetic images, that are modeled after real digital microscopy images of cells. The results show that the method is able to detect point like fluorescent signals as correct as a manual operator.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-67588 (URN)
Conference
Swedish Society for Automated Image Analysis (SSBA) Symposium on Image Analysis, Uppsala, Sweden, March 2004.
Note

The proccedings of the Swedish Society for Automated Image Analysis (SSBA).

Available from: 2005-05-25 Created: 2005-05-25 Last updated: 2018-12-18
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