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  • 251.
    Hult, R.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation of T1-MRI of the Human Cortex Using a 3D Grey-level Morphology Approach2003In: Image Analysis: 13th Scandinavian Conference, SCIA2003 , Halmstad, Sweden, 2003, p. 462–469-Chapter in book (Other academic)
  • 252.
    Hult, R.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Agartz, I.
    Segmentation of Multimodal MRI of Hippocampus Using 3D Greylevel Morphology Combined with Artificial Neural NetworksManuscript (Other academic)
  • 253.
    Hult, R.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, E.
    Combined Visualisation of Functional and Anatomical Brain Images2001In: Proceedings of 12th Scandinavian Conference on Image Analysis, SCIA 2001, Bergen, Norway, 2001, p. 84–89-Chapter in book (Other academic)
  • 254.
    Hult, R.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, E.
    Combined Visualisation of Functional and Anatomical Brain Images2001Conference paper (Refereed)
    Abstract [en]

    Fusion of multimodality images refers to not only the registration of the images

  • 255.
    Hult, R.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, E.
    Grey-Level Morphology Based Segmentation of T1-MRI of the Human Cortex2001Conference paper (Refereed)
    Abstract [en]

    In this paper an algorithm for fully automatic segmentation of the cortex from

  • 256.
    Hult, R.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, E.
    Thurfjell, L.
    Segmentation of the Brain in MRI Using Grey Level Morphology and Propagation of Information1999In: Proceedings of 11th Scandinavian Conference on Image Analysis, SCIA’99, Kangerlussuaq, Greenland, 1999, p. 367–373-Chapter in book (Other academic)
  • 257.
    Hult, R.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, E.
    Thurfjell, L.
    Surface Construction Especially Suited for Visualisation of Thin Structures1997In: Proceedings of 10th Scandinavian Conference on Image Analysis, SCIA'97, 1997, p. 2149–2154-Chapter in book (Other academic)
  • 258.
    Hult, R.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Varnäs, K.
    Hall, H.
    Image Analysis of Co-registered Autoradiographic Human Whole Hemisphere SectionsManuscript (Other academic)
  • 259.
    Hult, Roger
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Grey-level Morphology Based Segmentation of MRI of the Human Cortex and Applications on Visualisation2001Licentiate thesis, monograph (Other scientific)
  • 260.
    Hult, Roger
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Grey-Level Morphology Combined with an Artificial Neural Networks Aproach for Multimodal Segmentation of the Hippocampus2003Conference paper (Refereed)
    Abstract [en]

    This paper presents an algorithm that continues segmentation from a semi automatic artificial neural network (ANN) segmentation of the hippocampus of registered T1-weighted and T2-weighted MRI data. Due to the morphological complexity of the hippocampus a

  • 261.
    Hult, Roger
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Segmentation and Visualisation of Human Brain Structures2003Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis the focus is mainly on the development of segmentation techniques for human brain structures and of the visualisation of such structures. The images of the brain are both anatomical images (magnet resonance imaging (MRI) and autoradigraphy) and functional images that show blood flow (functional magnetic imaging (fMRI), positron emission tomography (PET), and single photon emission tomograpy (SPECT)). When working with anatomical images, the structures segmented are visible as different parts of the brain, e.g. the brain cortex, the hippocampus, or the amygdala. In functional images, the activity or the blood flow that be seen.

    Grey-level morphology methods are used in the segmentations to make tissue types in the images more homogenous and minimise difficulties with connections to outside tissue. A method for automatic histogram thresholding is also used. Furthermore, there are binary operations such as logic operation between masks and binary morphology operations.

    The visualisation of the segmented structures uses either surface rendering or volume rendering. For the visualisation of thin structures, surface rendering is the better choice since otherwise some voxels might be missed. It is possible to display activation from a functional image on the surface of a segmented cortex.

    A new method for autoradiographic images has been developed, which integrates registration, background compensation, and automatic thresholding to getfaster and more realible results than the standard techniques give.

    List of papers
    1. Surface Construction Especially Suited for Visualisation of Thin Structures
    Open this publication in new window or tab >>Surface Construction Especially Suited for Visualisation of Thin Structures
    1997 (English)In: Proceedings of 10th Scandinavian Conference on Image Analysis, SCIA'97, 1997, p. 2149–2154-Chapter in book (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90808 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
    2. Automatic Detection of Hypoperfused Areas in SPECT Brain Scans
    Open this publication in new window or tab >>Automatic Detection of Hypoperfused Areas in SPECT Brain Scans
    Show others...
    1998 (English)In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 45, no 4, p. 2149-2154Article in journal (Refereed) Published
    Abstract [en]

    Describes a method for automatic identification of areas with perfusion changes in SPECT brain images. An intersubject registration technique is used to stereotactically register images from a selected control group allowing for reference images to be created by averaging the subjects image data. An individual SPECT brain scan can be brought into registration with the reference image and comparison to the normal reference group can be made by subtracting the two volumes. Furthermore, since the variance in the reference group is known, a z-score image or an image coded in standard deviations, can be computed. The SPECT reference volume is defined in the same coordinate system as a brain atlas, and anatomical labeling of areas of interest is possible. The authors show results from the creation of an average image based on 11 individuals and from its comparison with pathological cases

    Keywords
    POSITRON EMISSION TOMOGRAPHY, PET IMAGES, ATLAS, LOCALIZATION, LINE
    National Category
    Information Systems
    Identifiers
    urn:nbn:se:uu:diva-90809 (URN)10.1109/23.708327 (DOI)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2018-01-13Bibliographically approved
    3. Segmentation of the Brain in MRI Using Grey Level Morphology and Propagation of Information
    Open this publication in new window or tab >>Segmentation of the Brain in MRI Using Grey Level Morphology and Propagation of Information
    1999 (English)In: Proceedings of 11th Scandinavian Conference on Image Analysis, SCIA’99, Kangerlussuaq, Greenland, 1999, p. 367–373-Chapter in book (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90810 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
    4. Combined Visualisation of Functional and Anatomical Brain Images
    Open this publication in new window or tab >>Combined Visualisation of Functional and Anatomical Brain Images
    2001 (English)In: Proceedings of 12th Scandinavian Conference on Image Analysis, SCIA 2001, Bergen, Norway, 2001, p. 84–89-Chapter in book (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90811 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
    5. Grey-level Morphology Based Segmentation of MRI of the Human Cortex
    Open this publication in new window or tab >>Grey-level Morphology Based Segmentation of MRI of the Human Cortex
    2001 (English)In: Proceedings of ICIAP’01 11th International Conference on Image Analysis and Processing, Palermo, Italy, 2001, p. 578–583-Chapter in book (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90812 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
    6. Segmentation of T1-MRI of the Human Cortex Using a 3D Grey-level Morphology Approach
    Open this publication in new window or tab >>Segmentation of T1-MRI of the Human Cortex Using a 3D Grey-level Morphology Approach
    2003 (English)In: Image Analysis: 13th Scandinavian Conference, SCIA2003 , Halmstad, Sweden, 2003, p. 462–469-Chapter in book (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90813 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
    7. Grey-level Morphology Combined with an Artificial Networks Approach for Multimodal Segmentation of the Hippocampus
    Open this publication in new window or tab >>Grey-level Morphology Combined with an Artificial Networks Approach for Multimodal Segmentation of the Hippocampus
    (English)In: Proceedings of ICIAP’03 12th International Conference on ImageChapter in book (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90814 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-03-01Bibliographically approved
    8. Image Analysis of Co-registered Autoradiographic Human Whole Hemisphere Sections
    Open this publication in new window or tab >>Image Analysis of Co-registered Autoradiographic Human Whole Hemisphere Sections
    Manuscript (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90815 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-01-13Bibliographically approved
    9. Segmentation of Multimodal MRI of Hippocampus Using 3D Greylevel Morphology Combined with Artificial Neural Networks
    Open this publication in new window or tab >>Segmentation of Multimodal MRI of Hippocampus Using 3D Greylevel Morphology Combined with Artificial Neural Networks
    Manuscript (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-90816 (URN)
    Available from: 2003-09-15 Created: 2003-09-15 Last updated: 2010-01-13Bibliographically approved
  • 262.
    Hult, Roger
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation of T1-MRI of the Human Cortex Using a 3DGrey-level Morphology Approach2003Conference paper (Refereed)
    Abstract [en]

    In this paper, an algorithm for fully automatic segmentation of the cortex from T1-weighted transversal, coronal, or sagittal MRI data is presented. The segmentation algorithm uses a histogram-based method to find accurate threshold values. There are four

  • 263.
    Hult, Roger
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Thurfjell, Ewert
    Surface Construction Especially Suited for Visualization of Thin Structures1997In: SCIA'97, Pattern Recognition Society of Finland , 1997, p. 359-363Conference paper (Refereed)
  • 264.
    Hult, Roger
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Thurfjell, Lennart
    Segmentation of the Brain in MRI Using Grey Level Morphology and Propagation of Information1999In: Proceedings of SCIA'99, Pattern Recognition Society of Denmark, Lyngby , 1999, p. 367-373Conference paper (Refereed)
    Abstract [en]

    An important step in the analysis of 3D MRI brain images is to segment the cortex from surrounding tissue. In this paper we present an algorithm for fully automatic segmentation of the cortex from T1-weighted MRI data. The automatic segmentation starts wi

  • 265.
    Häggman, M
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Nordin, Bo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Mattson, S
    Busch, Christer
    Morphometric studies of intra-prostatic volume relationships in localized prostatic cancer1997In: British Journal of Urology, ISSN 0007-1331, E-ISSN 1365-2176, Vol. 80, no 4, p. 612-617Article in journal (Refereed)
    Abstract [en]

    Objectives To further characterize patterns of tumour growth and the distribution of markers for the aggressiveness of prostate cancer by assessing the relationships among the volume of the 'index' tumour and that of the remaining foci, with pathological

  • 266.
    Högberg, A.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ballerini, L.
    Soussi, B.
    Lindgård, A.
    Rakotonirainy, O.
    Borgefors, G.
    Lundström, K.
    Bylund, A.-C.
    A novel method for quantitative fat analysis in meat by in vivo Magnetic Resonance Imaging2000In: 46th International Conference on Meat Science and Technology, Buenos Aires, Argentina, 2000, p. 372-373Conference paper (Other scientific)
  • 267.
    Högberg, A.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ballerini, L.
    Soussi, B.
    Lindgård, A.
    Rakotonirainy, O.
    Borgefors, G.
    Lundström, K.
    Bylund, A.-C.
    A novel method for quantitative fat analysis in meat by in vivo Magnetic Resonance Imaging2000In: Livsmedel 2000, Nationella livsmedelsforskardaga, Uppsala, Sweden, 2000Conference paper (Other scientific)
  • 268.
    Höglund, Anna-Stina
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Liu, Jingxia
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Karlsson, Patrick
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsson, Lars
    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 disease2007In: Biophysical Journal: 637A-637A Suppl. S, 2007Conference paper (Other scientific)
  • 269.
    Höglund, Stefan
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Chemistry, Department of Photochemistry and Molecular Science. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Su, Jin
    Sundin Reneby, Sara
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Chemistry, Department of Photochemistry and Molecular Science. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Végvári, Ákos
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Chemistry, Department of Photochemistry and Molecular Science. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hjertén, Stellan
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Chemistry, Department of Photochemistry and Molecular Science. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sintorn, Ida-Maria
    Interfaculty Units, Centre for Image Analysis. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Foster, Hillary
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Chemistry, Department of Photochemistry and Molecular Science. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wu, Yi-Ping
    Nyström, Ingela
    Interfaculty Units, Centre for Image Analysis. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Vahlne, Anders
    Tripeptide Interference with Human Immunodeficiency Virus Type 1 Morphogenesis2002In: Antimicrobial Agents and Chemotherapy, Vol. 46, no 11, p. 3597-3605Article in journal (Refereed)
  • 270. Ibrahim, Muhammad Talal
    et al.
    Khan, M. Aurangzeb
    Alimgeer, Khurram Saleem
    Niazi, M Khalid Khan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Taj, Imtiaz A.
    Guan, Ling
    Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification2010In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 43, no 8, p. 2817-2832Article in journal (Refereed)
    Abstract [en]

    In general, shape of an on-line signature is used as a single discriminating feature. Sometimes shape of signature is used alone for verification purposes and sometimes it is used in combination with some other dynamic features such as velocity, pressure and acceleration. The shape of an on-line signature is basically formed due to the wrist and fingers movements where the wrist movement is represented by the horizontal trajectory and the movement of the fingers is represented by vertical trajectory. As the on-line signature is formed due to the combination of two movements that are essentially independent of each other, it will be more effective to use them as two separate discriminating features. Based on this observation, we propose to use these trajectories in isolation by first decomposing the pressure and velocity profiles into two partitions and then extracting the underlying horizontal and vertical trajectories. So the overall process can be thought as the process which exploits the inter-feature dependencies by decomposing signature trajectories depending upon pressure and velocity information and performs verification on each partition separately. As a result, we are able to extract eight discriminating features and among them the most stable discriminating feature is used in verification process. Further Principal Component Analysis (PCA) has been proposed to make the signatures rotation invariant. Experimental results demonstrate superiority of our approach in on-line signature verification in comparison with other techniques.

  • 271. Jahangir Tafrechi, Roshan S.
    et al.
    van de Rijke, Frans M.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsson, Chatarina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Sloos, Willem C. R.
    van de Sande, Marchien
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Janssen, George M. C.
    Raap, Anton K.
    Single-cell A3243G mitochondrial DNA mutation load assays for segregation analysis2007In: Journal of Histochemistry and Cytochemistry, ISSN 0022-1554, E-ISSN 1551-5044, Vol. 55, no 11, p. 1159-1166Article in journal (Refereed)
    Abstract [en]

    Segregation of mitochondrial DNA (mtDNA) is an important underlying pathogenic factor in mtDNA mutation accumulation in mitochondrial diseases and aging, but the molecular mechanisms of mtDNA segregation are elusive. Lack of high-throughput single-cell mutation load assays lies at the root of the paucity of studies in which, at the single-cell level, mitotic mtDNA segregation patterns have been analyzed. Here we describe development of a novel fluorescence-based, non-gel PCR restriction fragment length polymorphism method for single-cell A3243G mtDNA mutation load measurement. Results correlated very well with a quantitative in situ Padlock/rolling circle amplification–based genotyping method. In view of the throughput and accuracy of both methods for single-cell A3243G mtDNA mutation load determination, we conclude that they are well suited for segregation analysis.

  • 272. Jansen, N
    et al.
    Nejdl, W
    Olbrich, S
    Seipel, S
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    CoVASE: Collaborative Visualization for Constructivist Learning2003In: Proceedings of CSCL Conf. 2003, 2003, p. 249-253Conference paper (Other (popular scientific, debate etc.))
  • 273.
    Jarkrans, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for Cell Image Analysis in Cytology and Pathology1996Doctoral thesis, comprehensive summary (Other academic)
  • 274.
    Jarkrans, Torsten
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Vasko, Janos
    Bengtsson, Ewert
    Choi, Heung-Kook
    Malmström, Per-Uno
    Wester, Kenneth
    Busch, Christer
    Grading of transitional cell bladder carcinoma by image analysis of histological sections  1995In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 8, no 2, p. 135-158Article in journal (Refereed)
    Abstract [en]

    Image analysis of histological sections was used to achieve a more objective malignancy grading of transitional cell carcinoma of the bladder. Images from Feulgen-stained sections from a clinical material of 197 tumours were analyzed. Features at various

  • 275.
    Jarvius, Malin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Paulsson, Janna
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Leuchowius, Karl-Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, Ann-Catrin
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Gullberg, Mats
    Botling, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Sjöblom, Tobias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Markova, Boyka
    Östman, Arne
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    In situ detection of phosphorylated platelet-derived growth factor receptor beta using a generalized proximity ligation method2007In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 6, no 9, p. 1500-1509Article in journal (Refereed)
    Abstract [en]

    Improved methods are needed for in situ characterization of post-translational modifications in cell lines and tissues. For example, it is desirable to monitor the phosphorylation status of individual receptor tyrosine kinases in samples from human tumors treated with inhibitors to evaluate therapeutic responses. Unfortunately the leading methods for observing the dynamics of tissue post-translational modifications in situ, immunohistochemistry and immunofluorescence, exhibit limited sensitivity and selectivity. Proximity ligation assay is a novel method that offers improved selectivity through the requirement of dual recognition and increased sensitivity by including DNA amplification as a component of detection of the target molecule. Here we therefore established a generalized in situ proximity ligation assay to investigate phosphorylation of platelet-derived growth factor receptor β (PDGFRβ) in cells stimulated with platelet-derived growth factor BB. Antibodies specific for immunoglobulins from different species, modified by attachment of DNA strands, were used as secondary proximity probes together with a pair of primary antibodies from the corresponding species. Dual recognition of receptors and phosphorylated sites by the primary antibodies in combination with the secondary proximity probes was used to generate circular DNA strands; this was followed by signal amplification by replicating the DNA circles via rolling circle amplification. We detected tyrosine phosphorylated PDGFRβ in human embryonic kidney cells stably overexpressing human influenza hemagglutinin-tagged human PDGFRβ in porcine aortic endothelial cells transfected with the β-receptor, but not in cells transfected with the α-receptor, and also in immortalized human foreskin fibroblasts, BJ hTert, endogenously expressing the PDGFRβ. We furthermore visualized tyrosine phosphorylated PDGFRβ in tissue sections from fresh frozen human scar tissue undergoing wound healing. The method should be of great value to study signal transduction, screen for effects of pharmacological agents, and enhance the diagnostic potential in histopathology.

  • 276. Jensen, N
    et al.
    Seipel, Stefan
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Människa-datorinteraktion.
    von Voigt, S
    Raasch, S
    Olbrich, S
    Nejdl, W
    Development of a Virtual Laboratory System for Science Education and the Study of Collaborative Action2004In: AACE ED Media Conference 2004, 2004, p. 21-26Conference paper (Refereed)
  • 277. Johansson, Henrik
    et al.
    Svedlund, Per Erik
    Siira, Erik
    Sarve, Hamid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    A Cost-Efficient and Automatic Digitization Workflow Using Commodity Hardware and Image Analysis2010In: Archiving 2010, Den Haag: Society for Imaging Science and Technology , 2010Conference paper (Other academic)
  • 278.
    Johansson, Thomas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Image Analysis Algorithms on General Purpose Parallel Architectures1994Doctoral thesis, monograph (Other academic)
  • 279.
    Johansson, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Data parallel supervised classification algorithms on multispectral images1996In: International journal of pattern recognition and artificial intelligence, ISSN 0218-0014, Vol. 10, no 7, p. 751-767Article in journal (Refereed)
    Abstract [en]

    In remote sensing the intensities from a multispectral image are used in a classification scheme to distinguish different ground cover from each other. An example is given where different soil types are classified. A digitized complete scene from a satell

  • 280. Jonker, Pieter P.
    et al.
    Svensson, Stina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    The Generation of N Dimensional Shape Primitives2003In: Discrete Geometry for Computer Imagery: 11th International Conference, DGCI 2003, Naples, Italy, 2003Conference paper (Refereed)
    Abstract [en]

    This paper describes a method to accelerate the generation of shape primitives for N-dimensional images X N . These shape primitives can be used in conditions for topology preserving erosion or skeletonization in N-dimensional images. The method is based on the possibility to describe primitives for intrinsic dimensions by quadratic equations of the form

  • 281.
    Jonsson, C
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Pagani, M
    Ingvar, M
    Thurfjell, Lennart
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Kimiaei, S
    Jacobsson, H
    Larsson, SA
    Resting state rCBF mapping with single-photon emission tomography and positron emission tomography: magnitude and origin of differences1998In: European Journal of Nuclear Medicine, ISSN 0340-6997, E-ISSN 1432-105X, Vol. 25, no 2, p. 157-165Article in journal (Other academic)
    Abstract [en]

    Single-photon emission tomography (SPET), using technetium-99m hexamethylpropylene amine oxime, and positron emission tomography (PET), using oxygen-15 butanol were compared in six healthy male volunteers with regard to the mapping of resting state region

  • 282.
    Jonsson, C.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Pagani, M.
    Johansson, L.
    Thurfjell, L.
    Jacobsson, H.
    Larsson, S.A.
    Reproducibility and repeatability of 99Tcm-HMPAr CBF SPET in normal subjectsat rest using brain atlas matching2000In: Nuclear Medicine Communications, Vol. 21, no 1, p. 9-18Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to assess regional cerebral blood flow (rCBF) in

  • 283.
    Julia Åhlén, Bengtsson Ewert, Lindell Tommy
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Color Correction of Underwater Images Based on Estimation of Diffuse Attenuation Coefficients2003Conference paper (Refereed)
  • 284.
    Karlsson Edlund, Patrick
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Introduction to the Mean-Shift Procedure: Filtering and Segmentation2008Report (Other academic)
  • 285.
    Karlsson Edlund, Patrick
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Methods and models for 2D and 3D image analysis in microscopy, in particular for the study of muscle cells2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems.

    The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells.

    A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender.

    A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification.

    An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.

    List of papers
    1. Delayed effects on plasma concentration of testosterone and testicular morphology by intramuscular low-dose di(2-ethylhexyl)phthalate or oestradiol benzoate in the prepubertal boar
    Open this publication in new window or tab >>Delayed effects on plasma concentration of testosterone and testicular morphology by intramuscular low-dose di(2-ethylhexyl)phthalate or oestradiol benzoate in the prepubertal boar
    Show others...
    2005 (English)In: Theriogenology, ISSN 0093-691X, E-ISSN 1879-3231, Vol. 64, no 5, p. 1170-1184Article in journal (Refereed) Published
    Abstract [en]

    The immediate and delayed effects of prepubertal exposure to di(2-ethylhexyl)phthalate (DEHP) or oestradiol benzoate on the plasma concentrations of testosterone, oestradiol and LH, as well as testicular morphology were examined in prepubertal boars. In a split litter design experiment, prepubertal boars were intramuscularly exposed to DEHP, oestradiol or vehicle during five weeks, starting at six weeks of age. The dose of DEHP was 50 mg/kg of bodyweight twice weekly, which is in the same range as recently used oral doses in rodents. Oestradiol-benzoate was administered at 0.25 mg/kg of bodyweight twice weekly. One set of animals was examined immediately after the exposure, and the other set was examined at an age of 7.5 months. During the exposure period concentrations of LH in plasma were lower (p = 0.02) in the oestradiol-treated animals than in the control group. In the group exposed to oestradiol, the relative to the body weight of the testicles tended to be lower (p = 0.07) than control immediately after five weeks of exposure, and the relative to the body weight of the seminal vesicles tended to be lower (p = 0.05) than control at 7.5 months of age. In the DEHP-exposed group an elevated (p = 0.005) concentration of testosterone and increased (p = 0.04) area of the Leydig cells in the testicles compared to the control group were seen at 7.5 months of age. These data suggest that DEHP early in life causes delayed effects on the reproductive system in the adult.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-97425 (URN)10.1016/j.theriogenology.2005.02.003 (DOI)
    Available from: 2008-08-29 Created: 2008-08-29 Last updated: 2017-12-14Bibliographically approved
    2. Segmentation and separation of point like fluorescent markers in digital images
    Open this publication in new window or tab >>Segmentation and separation of point like fluorescent markers in digital images
    2004 (English)In: ISBI2004, 2004, p. 1291-1294Conference paper, Published paper (Refereed)
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-97426 (URN)10.1109/ISBI.2004.1398782 (DOI)0-7803-8388-5 (ISBN)
    Conference
    IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, VA, USA, April 2004
    Available from: 2008-08-29 Created: 2008-08-29 Last updated: 2018-12-18
    3. Finding cells, finding molecules, finding patterns
    Open this publication in new window or tab >>Finding cells, finding molecules, finding patterns
    Show others...
    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
    4. Non-uniform 3D distance transform for anisotropic signal correction in confocal image volumes of skeletal muscle cell nuclei
    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
    5. Effects of ageing and gender on the spatial organization of nuclei in single human skeletal muscle cells
    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
    Show others...
    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
    6. Myonuclear domain size and myosin isoform expression in muscle fibres from mammals representing a 100 000-fold difference in body size
    Open this publication in new window or tab >>Myonuclear domain size and myosin isoform expression in muscle fibres from mammals representing a 100 000-fold difference in body size
    Show others...
    2009 (English)In: Experimental Physiology, ISSN 0958-0670, E-ISSN 1469-445X, Vol. 94, no 1, p. 117-129Article in journal (Refereed) Published
    Abstract [en]

    This comparative study of myonuclear domain (MND) size in mammalian species representing a 100 000-fold difference in body mass, ranging from 25 g to 2500 kg, was undertaken to improve our understanding of myonuclear organization in skeletal muscle fibres. Myonuclear domain size was calculated from three-dimensional reconstructions in a total of 235 single muscle fibre segments at a fixed sarcomere length. Irrespective of species, the largest MND size was observed in muscle fibres expressing fast myosin heavy chain (MyHC) isoforms, but in the two smallest mammalian species studied (mouse and rat), MND size was not larger in the fast-twitch fibres expressing the IIA MyHC isofom than in the slow-twitch type I fibres. In the larger mammals, the type I fibres always had the smallest average MND size, but contrary to mouse and rat muscles, type IIA fibres had lower mitochondrial enzyme activities than type I fibres. Myonuclear domain size was highly dependent on body mass in the two muscle fibre types expressed in all species, i.e. types I and IIA. Myonuclear domain size increased in muscle fibres expressing both the β/slow (type I; r= 0.84, P < 0.001) and the fast IIA MyHC isoform (r= 0.90; P < 0.001). Thus, MND size scales with body size and is highly dependent on muscle fibre type, independent of species. However, myosin isoform expression is not the sole protein determining MND size, and other protein systems, such as mitochondrial proteins, may be equally or more important determinants of MND size.

    National Category
    Physiology
    Identifiers
    urn:nbn:se:uu:diva-87940 (URN)10.1113/expphysiol.2008.043877 (DOI)000261961800014 ()18820003 (PubMedID)
    Available from: 2009-01-15 Created: 2009-01-15 Last updated: 2018-12-02Bibliographically approved
    7. Introduction to the Mean-Shift Procedure: Filtering and Segmentation
    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
  • 286.
    Karlsson Edlund, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Non-uniform 3D distance transform for anisotropic signal correction in confocal image volumes of skeletal muscle cell nuclei2008In: Proc. 5th International Symposium on Biomedical Imaging, Piscataway, NJ: IEEE , 2008, p. 1363-1366Conference paper (Refereed)
  • 287.
    Karlsson, Hans
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry, Quantum Chemistry.
    Nyström, IngelaUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    UPPMAX Progress Report2008Collection (editor) (Other (popular science, discussion, etc.))
  • 288.
    Karlsson, Irene
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, Department of Ecology and Evolution. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, Department of Ecology and Evolution, Limnology.
    Karlsson, Patrick
    Interfaculty Units, Centre for Image Analysis. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, Department of Ecology and Evolution, Limnology.
    Strömbeck, Niklas
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, Department of Ecology and Evolution. Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, Department of Ecology and Evolution, Limnology.
    A comparison of manual enumeration and image analysis of Gloeotrichia echinulata2003In: Verhandlungen IVL: 28th Congress in Melbourne 2001, 2003, p. 458-Conference paper (Other scientific)
  • 289.
    Karlsson, Patrick
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    A simple and possibly efficient approach to Automatic License Plate Recognition2002In: Proceedings SSAB'02: Symposium on Image Analysis, 2002, p. 185-Conference paper (Other scientific)
    Abstract [en]

    This paper addresses the problems associated with automatically detecting and reading license plates in a set of images. After a structural analysis of the problem, together with the formulation of a relaxed approach, a simple and possibly efficient solution is applied to images with license plates from the United Arab Emirates, and part of yhe system is also applied to images containing Swedish license plates.

  • 290.
    Karlsson, Patrick
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Automatic license plate detection2001Report (Other scientific)
  • 291.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Correction in 3D Confocal Images2008In: Proceedings of the 2008 Symposium on Image Analysis, 2008, p. 31-34Conference paper (Other academic)
  • 292. Karlsson, Patrick
    et al.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation and separation of point like fluorescent markers in digital images2004In: ISBI2004, 2004, p. 1291-1294Conference paper (Refereed)
  • 293.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Höglund, Anna-Stina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Klinisk neurofysiologi.
    Liu, Jingxia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Klinisk neurofysiologi.
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Klinisk neurofysiologi.
    Analysis of Skeletal Fibers in Three Dimensional Images2007In: Medicinteknikdagarna 2007, 2007, p. 1-Conference paper (Other (popular science, discussion, etc.))
  • 294.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Höglund, Anna-Stina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Clinical Neurophysiology.
    Liu, Jingxia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Clinical Neurophysiology.
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Clinical Neurophysiology.
    Analysis of Skeletal Fibers in Three Dimensional Images: Methodological considerations2007In: XXXVIth European Muscle Conference of the European Society for Muscle Research: European Muscle Conference 2007, 2007, p. 130-Conference 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.

  • 295.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation of point-like fluorescent markers2004In: Proceedings: Symposium on Image Analysis, 2004, p. 146-149Conference 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.

  • 296. Kim, Tae-Yun
    et al.
    Choi, Hyun-Ju
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hwang, Hae-Gil
    Choi, Heung-Kook
    Three-dimensional texture analysis of renal cell carcinoma cell nuclei for computerized automatic grading2010In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 34, no 4, p. 709-716Article in journal (Refereed)
    Abstract [en]

    The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we analyzed the three-dimensional chromatin texture of cell nuclei based on digital image cytometry. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray level co-occurrence matrices and 3D run length matrices. Finally, to demonstrate the suitability of 3D texture features for classification, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%.

  • 297.
    Kjellin, Andreas
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.
    Winkler Pettersson, Lars
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Human-Computer Interaction.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lind, Mats
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.
    Different levels of 3D: An evaluation of visualized discrete spatiotemporal data in space-time cubes2010In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 9, no 2, p. 152-164Article in journal (Refereed)
  • 298.
    Kjellin, Andreas
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.
    Winkler Pettersson, Lars
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Human-Computer Interaction.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lind, Mats
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.
    Evaluating 2D and 3D Visualizations of Spatiotemporal Information2010In: ACM Transactions on Applied Perception, ISSN 1544-3558, Vol. 7, no 3, p. 19:1-23Article in journal (Refereed)
  • 299.
    Knudsen, T.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hamid Muhammed, H.
    Olsen, B. P.
    A Comparison of Neuro-Fuzzy and Traditional Image Segmentation Methods for Automated Detection of Buildings in Aerial Photos2002In: Proceedings of PCV'02: PHOTOGRAMMETRIC COMPUTER VISION 2002, 2002Conference paper (Other scientific)
    Abstract [en]

    Using a set of colour-infrared aerial photos, we compare a newly developed neural net based clustering method with a method based on the classical ISODATA algorithm. The primary focus is on the detection of buildings and it shows that while the traditiona

  • 300. Knudsen, Thomas
    et al.
    Hamid Muhammed, Hamed
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Olsen, Brian Pilemann
    A Comparison of Neuro-Fuzzy and Traditional Image Segmentation Methods for Automated Detection of Buildings in Aerial Photos2002In: Proceedings of PCV'02: PHOTOGRAMMETRIC COMPUTER VISION 2002Article in journal (Refereed)
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