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  • 1.
    Adler, Jeremy
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Strand, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Parmryd, Ingela
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous2019In: Communications Biology, ISSN 2399-3642, Vol. 2, article id 12Article in journal (Refereed)
  • 2.
    Axelsson, Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sintorn, Ida-Maria
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Svensson, Stina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Individual pore segmentation in 3D volumes of fibrous materials2005In: SSBA Symposium on Image Analysis 2005, 2005Conference paper (Other scientific)
  • 3. Bajic, Buda
    et al.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gupta, Anindya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Pepic, Ivana
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement2018In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 921-925Conference paper (Refereed)
  • 4. Bernander, Karl B.
    et al.
    Gustavsson, Kenneth
    Selig, Bettina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Improving the stochastic watershed2013In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 34, no 9, p. 993-1000Article in journal (Refereed)
    Abstract [en]

    The stochastic watershed is an unsupervised segmentation tool recently proposed by Angulo and Jeulin. By repeated application of the seeded watershed with randomly placed markers, a probability density function for object boundaries is created. In a second step, the algorithm then generates a meaningful segmentation of the image using this probability density function. The method performs best when the image contains regions of similar size, since it tends to break up larger regions and merge smaller ones. We propose two simple modifications that greatly improve the properties of the stochastic watershed: (1) add noise to the input image at every iteration, and (2) distribute the markers using a randomly placed grid. The noise strength is a new parameter to be set, but the output of the algorithm is not very sensitive to this value. In return, the output becomes less sensitive to the two parameters of the standard algorithm. The improved algorithm does not break up larger regions, effectively making the algorithm useful for a larger class of segmentation problems.

  • 5.
    Borgefors, Gunilla
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Nyström, Ingela
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sintorn, Ida-Maria
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Strand, Robin
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wadelius, Lena
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Centre for Image Analysis Annual Report 20032004Report (Other (popular scientific, debate etc.))
  • 6.
    Christersson, Albert
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Nysjö, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Berglund, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Larsson, Sune
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Comparison of 2D radiography and a semi-automatic CT-based 3D method for measuring change in dorsal angulation over time in distal radius fractures2016In: Skeletal Radiology, ISSN 0364-2348, E-ISSN 1432-2161, Vol. 45, no 6, p. 763-769Article in journal (Refereed)
    Abstract [en]

    Objective The aim of the present study was to compare the reliability and agreement between a computer tomography-based method (CT) and digitalised 2D radiographs (XR) when measuring change in dorsal angulation over time in distal radius fractures. Materials and methods Radiographs from 33 distal radius fractures treated with external fixation were retrospectively analysed. All fractures had been examined using both XR and CT at six times over 6 months postoperatively. The changes in dorsal angulation between the first reference images and the following examinations in every patient were calculated from 133 follow-up measurements by two assessors and repeated at two different time points. The measurements were analysed using Bland-Altman plots, comparing intra- and inter-observer agreement within and between XR and CT. Results The mean differences in intra- and inter-observer measurements for XR, CT, and between XR and CT were close to zero, implying equal validity. The average intra- and inter-observer limits of agreement for XR, CT, and between XR and CT were +/- 4.4 degrees, +/- 1.9 degrees and +/- 6.8 degrees respectively. Conclusions For scientific purpose, the reliability of XR seems unacceptably low when measuring changes in dorsal angulation in distal radius fractures, whereas the reliability for the semi-automatic CT-based method was higher and is therefore preferable when a more precise method is requested.

  • 7.
    Gupta, Anindya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Harrison, Philip J.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Pielawski, Nicolas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Kartasalo, Kimmo
    Partel, Gabriele
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Solorzano, Leslie
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Klemm, Anna H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Deep Learning in Image Cytometry: A Review2019In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 6, p. 366-380Article, review/survey (Refereed)
  • 8.
    Gupta, Anindya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bajic, Buda
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Pepic, Ivana
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Denoising of Short Exposure Transmission Electron Microscopy Images using CNN2018In: Swedish Symposium on Image Analysis, 2018Conference paper (Other academic)
  • 9. Gupta, Anindya
    et al.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    False positive reduction of cilia detected in low resolution TEM images using a convolutional neural network2017In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper (Other academic)
  • 10.
    Gupta, Anindya
    et al.
    Tallinn Univ Technol, TJ Seebeck Dept Elect, Tallinn, Estonia.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Vironova AB, Stockholm, Sweden.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images2017In: Image Analysis: Part I, Springer, 2017, p. 407-418Conference paper (Refereed)
    Abstract [en]

    Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.

  • 11.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kylberg, Gustav
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    An efficient descriptor based on radial line integration for fast non invariant matching and registration of microscopy images2017In: Advanced Concepts for Intelligent Vision Systems, Springer, 2017, p. 723-734Conference paper (Refereed)
  • 12.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sablina, Victoria A.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kylberg, Gustaf
    A fast Fourier based feature descriptor and a cascade nearest neighbour search with an efficient matching pipeline for mosaicing of microscopy images2018In: Pattern Recognition and Image Analysis, ISSN 1054-6618, Vol. 28, no 2, p. 261-272Article in journal (Refereed)
  • 13.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sablina, Victoria
    Kylberg, Gustav
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A Simple and Efficient Feature Descriptor for Fast Matching2015In: WSCG / [ed] V. Skala, 2015, p. 135-142Conference paper (Refereed)
  • 14.
    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)
  • 15.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A Note on: Invariant Features, Overfitting and Generalization Performance in Texture RecognitionManuscript (preprint) (Other academic)
  • 16.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern FeaturesArticle in journal (Refereed)
  • 17.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Evaluation of noise robustness for local binary pattern descriptors in texture classification2013In: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, no 17Article in journal (Refereed)
    Abstract [en]

    Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

  • 18.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Exploring Filter Banks Based on Orthogonal Moments for Texture RecognitionManuscript (preprint) (Other academic)
  • 19. Kylberg, Gustaf
    et al.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    On the influence of interpolation method on rotation invariance in texture recognition2016In: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, Vol. 2016, article id 17Article in journal (Refereed)
  • 20.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Refinement of Segmented Virus Particel Candidates in TEM Images2011Conference paper (Other academic)
  • 21.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Uppström, Mats
    Hedlund, Kjell-Olof
    Borgefors, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Segmentation of virus particle candidates in transmission electron microscopy images2012In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 245, no 2, p. 140-147Article in journal (Refereed)
    Abstract [en]

    In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

  • 22.
    Kylberg, Gustaf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Uppström, Mats
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Virus texture analysis using local binary patterns and radial density profiles2011In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications / [ed] San Martin, César; Kim, Sang-Woon, Springer Berlin/Heidelberg, 2011, p. 573-580Conference paper (Refereed)
    Abstract [en]

    We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

  • 23.
    Lidayová, Kristína
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gupta, Anindya
    Frimmel, Hans
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Smedby, Örjan
    Classification of cross-sections for vascular skeleton extraction using convolutional neural networks2017In: Medical Image Understanding and Analysis, Springer, 2017, p. 182-194Conference paper (Refereed)
    Abstract
  • 24.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    High-resolution reconstruction by feature distance minimization from multiple views of an object2015In: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, p. 29-34Conference paper (Refereed)
    Abstract [en]

    We present a method which utilizes advantages of fuzzy object representations and image processing techniques adjusted to them, to further increase efficient utilization of image information. Starting from a number of low-resolution images of affine transformations of an object, we create its suitably defuzzified high-resolution reconstruction. We evaluate the proposed method on synthetic data, observing its performance w.r.t. noise sensitivity, influence of the number of used low-resolution images, sensitivity to object variation and to inaccurate registration. Our aim is to explore applicability of the method to real image data acquired by Transmission Electron Microscopy, in a biomedical application we are currently working on.

  • 25.
    Majda, Mateusz
    et al.
    Swedish Univ Agr Sci, Dept Forest Genet & Plant Physiol, UPSC, S-90183 Umea, Sweden..
    Grones, Peter
    Swedish Univ Agr Sci, Dept Forest Genet & Plant Physiol, UPSC, S-90183 Umea, Sweden..
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Vain, Thomas
    Swedish Univ Agr Sci, Dept Forest Genet & Plant Physiol, UPSC, S-90183 Umea, Sweden..
    Milani, Pascale
    Univ Lyon, ENS Lyon, UCB Lyon 1, Lab Reprod & Dev Plantes,CNRS,INRA, F-69342 Lyon, France..
    Krupinski, Pawel
    Lund Univ, Dept Astron & Theoret Phys, Computat Biol & Biol Phys, Solvegatan 14A, S-22362 Lund, Sweden..
    Zagorska-Marek, Beata
    Univ Wroclaw, Inst Expt Biol, Dept Plant Dev Biol, Kanonia 6-8, PL-50328 Wroclaw, Poland..
    Viotti, Corrado
    Umea Univ, Dept Plant Physiol, UPSC, S-90187 Umea, Sweden.;Univ Potsdam, Inst Biochem & Biol, Plant Physiol, D-14476 Potsdam, Germany..
    Jonsson, Henrik
    Lund Univ, Dept Astron & Theoret Phys, Computat Biol & Biol Phys, Solvegatan 14A, S-22362 Lund, Sweden.;Univ Cambridge, Sainsbury Lab, Bateman St, Cambridge CB2 1LR, England.;Univ Cambridge, Dept Math & Theoret Phys, Cambridge CB3 0WA, England..
    Mellerowicz, Ewa J.
    Swedish Univ Agr Sci, Dept Forest Genet & Plant Physiol, UPSC, S-90183 Umea, Sweden..
    Hamant, Olivier
    Univ Lyon, ENS Lyon, UCB Lyon 1, Lab Reprod & Dev Plantes,CNRS,INRA, F-69342 Lyon, France..
    Robert, Stephanie
    Swedish Univ Agr Sci, Dept Forest Genet & Plant Physiol, UPSC, S-90183 Umea, Sweden..
    Mechanochemical Polarization of Contiguous Cell Walls Shapes Plant Pavement Cells2017In: Developmental Cell, ISSN 1534-5807, E-ISSN 1878-1551, Vol. 43, no 3, p. 290-304Article in journal (Refereed)
    Abstract [en]

    The epidermis of aerial plant organs is thought to be limiting for growth, because it acts as a continuous load-bearing layer, resisting tension. Leaf epidermis contains jigsaw puzzle piece-shaped pavement cells whose shape has been proposed to be a result of subcellular variations in expansion rate that induce local buckling events. Paradoxically, such local compressive buckling should not occur given the tensile stresses across the epidermis. Using computational modeling, we show that the simplest scenario to explain pavement cell shapes within an epidermis under tension must involve mechanical wall heterogeneities across and along the anticlinal pavement cell walls between adjacent cells. Combining genetics, atomic force microscopy, and immunolabeling, we demonstrate that contiguous cell walls indeed exhibit hybrid mechanochemical properties. Such biochemical wall heterogeneities precede wall bending. Altogether, this provides a possible mechanism for the generation of complex plant cell shapes.

  • 26.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Science for Life Laboratory, Uppsala, Sweden.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    A short feature vector for image matching: The Log-Polar Magnitude feature descriptor2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, article id e0188496Article in journal (Refereed)
    Abstract [en]

    The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images.

  • 27.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Minimal annotation training for segmentation of microscopy images2018In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 387-390Conference paper (Refereed)
  • 28.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Puigvert, Jordi Carreras
    Uppsala University, Science for Life Laboratory, SciLifeLab. Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Comparison of Flow Cytometry and Image-Based Screening for Cell Cycle Analysis2016In: Image Analysis And Recognition (ICIAR 2016) / [ed] Aurélio Campilho, Fakhri Karray, Springer, 2016, Vol. 9730, p. 623-630Conference paper (Refereed)
    Abstract [en]

    Quantitative cell state measurements can provide a wealth of information about mechanism of action of chemical compounds and gene functionality. Here we present a comparison of cell cycle disruption measurements from commonly used flow cytometry (generating onedimensional signal data) and bioimaging (producing two-dimensional image data). Our results show high correlation between the two approaches indicating that image-based screening can be used as an alternative to flow cytometry. Furthermore, we discuss the benefits of image informatics over conventional single-signal flow cytometry.

  • 29.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras Puigvert, Jordi
    Karolinska Inst, Dept Med Biochem & Biophys, Div Translat Med & Chem Biol, Stockholm, Sweden; ] Sci Life Lab, Stockholm, Sweden .
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 3, article id e0151554Article in journal (Refereed)
    Abstract [en]

    Image-based screening typically produces quantitative measurements of cell appearance. Large-scale screens involving tens of thousands of images, each containing hundreds of cells described by hundreds of measurements, result in overwhelming amounts of data. Reducing per-cell measurements to the averages across the image(s) for each treatment leads to loss of potentially valuable information on population variability. We present PopulationProfiler-a new software tool that reduces per-cell measurements to population statistics. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. We validate the tool by showing how PopulationProfiler can be used to analyze the effect of drugs that disturb the cell cycle, and compare the results to those obtained with flow cytometry.

  • 30.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Krona, Cecilia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Nelander, Sven
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma2018In: SLAS Discovery: Advancing Life Sciences R&D, ISSN 2472-5552, Vol. 23, no 10, p. 1030-1039Article in journal (Refereed)
    Abstract [en]

    Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.

  • 31.
    Nysjö, Johan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Christersson, Albert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Towards User-Guided Quantitative Evaluation of Wrist Fractures in CT Images2012In: Computer Vision and Graphics: ICCVG 2012 / [ed] Bolc, Leonard; Tadeusiewicz, Ryszard; Chmielewski, Leszek J; Wojciechowski, Konrad, Springer Berlin/Heidelberg, 2012, p. 204-211Conference paper (Refereed)
    Abstract [en]

    The wrist is the most common location for long-bone fractures in humans. To evaluate the healing process of such fractures, it is of interest to measure the fracture displacement, particularly the angle between the joint line and the long axis of the fractured long bone. We propose to measure this angle in 3D computed tomography (CT) images of fractured wrists. As a first step towards this goal, we here present a fast and precise semi-automatic method for determining the long axis of the radius bone in CT images. To facilitate user interaction in 3D, we utilize stereo graphics, head tracking, 3D input, and haptic feedback.

  • 32.
    Nysjö, Johan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Christersson, Albert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Larsson, Sune
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Precise 3D Angle Measurements in CT Wrist Images2013In: Image Analysis and Processing – ICIAP 2013: Part II, Springer Berlin/Heidelberg, 2013, p. 479-488Conference paper (Refereed)
    Abstract [en]

    The clinically established method to assess the displacement of a distal radius fracture is to manually measure two reference angles,the dorsal angle and the radial angle, in consecutive 2D X-ray images of the wrist. This approach has the disadvantage of being sensitive to operator errors since the measurements are performed on 2D projections of a 3D structure. In this paper, we present a semi-automatic system for measuring relative changes in the dorsal angle in 3D computed tomography (CT) images of fractured wrists. We evaluate the proposed 3D measurement method on 28 post-operative CT images of fractured wrists and compare it with the radiographic 2D measurement method used in clinical practice. The results show that our proposed 3D measurement method has a high intra- and inter-operator precision and is more precise and robust than the conventional 2D measurement method.

  • 33.
    Nysjö, Johan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    BoneSplit - A 3D Texture Painting Tool for Interactive Bone Separation in CT Images2015In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 23, no 2, p. 157-166Article in journal (Refereed)
    Abstract [en]

    We present an efficient interactive tool for separating collectively segmented bones and bone fragments in 3D computed tomography (CT) images. The tool, which is primarily intended for virtual cranio-maxillofacial (CMF) surgery planning, combines direct volume rendering with an interactive 3D texture painting interface to enable quick identification and marking of individual bone structures. The user can paint markers (seeds) directly on the rendered bone surfaces as well as on individual CT slices. Separation of the marked bones is then achieved through the random walks segmentation algorithm, which is applied on a graph constructed from the collective bone segmentation. The segmentation runs on the GPU and can achieve close to real-time update rates for volumes as large as 512^3. Segmentation editing can be performed both in the random walks segmentation stage and in a separate post-processing stage using a local 3D editing tool. In a preliminary evaluation of the tool, we demonstrate that segmentation results comparable with manual segmentations can be obtained within a few minutes.

  • 34.
    Parmryd, Ingela
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Adler, Jeremy
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Cancer and Vascular Biology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Strand, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Movement on Uneven Surfaces Displays Characteristic Features of Hop Diffusion2013In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 104, no 2, p. 524A-524AArticle in journal (Other academic)
  • 35.
    Schmidt, Linnéa
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Baskaran, Sathishkumar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Johansson, Patrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Padhan, Narendra
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Matuszewski, Damian J.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Green, Lydia C.
    Elfineh, Ludmila
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wee, Shimei
    Häggblad, Maria
    Martens, Ulf
    Westermark, Bengt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Forsberg-Nilsson, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Uhrbom, Lene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Claesson-Welsh, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andäng, Michael
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lundgren, Bo
    Lönnstedt, Ingrid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Krona, Cecilia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nelander, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Case-specific potentiation of glioblastoma drugs by pterostilbene2016In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 7, no 45, p. 73200-73215Article in journal (Refereed)
  • 36.
    Sintorn, Ida-Maria
    et al.
    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.
    Bischof, Leanne
    CSIRO Mathematical and Information Sciences.
    Buckley, Michael
    CSIRO Mathematical and Information Sciences.
    Jackway, Paul
    CSIRO Mathematical and Information Sciences.
    Haggarty, Stephen
    BROAD Institute of Harvard and MIT.
    Gradient based intensity normalization2010In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 240, no 3, p. 249-258Article in journal (Refereed)
    Abstract [en]

    Intensity normalization is important in quantitative image analysis, especially when extracting features based on intensity. In automated microscopy, particularly in large cellular screening experiments, each image contains objects of similar type (e.g. cells) but the object density (number and size of the objects) may vary markedly from image to image. Standard intensity normalization methods, such as matching the grey-value histogram of an image to a target histogram from, i.e. a reference image, only work well if both object type and object density are similar in the images to be matched. This is typically not the case in cellular screening and many other types of images where object type varies little from image to image, but object density may vary dramatically. In this paper, we propose an improved form of intensity normalization which uses grey-value as well as gradient information. This method is very robust to differences in object density. We compare and contrast our method with standard histogram normalization across a range of image types, and show that the modified procedure performs much better when object density varies between images.

  • 37.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Shape based identification of proteins in volume images2005In: Image Analysis: 14th Scandinavian Conference on Image Analysis, SCIA 2005, 2005, p. 253-262Conference paper (Refereed)
    Abstract [en]

    A template based matching method, adapted to the application of identifying individual proteins of a certain kind in volume images, is presented. Grey-level and gradient megnitude information is combined in the watershed algorithm to extract stable borders. These are used in a subsequent hierarchical matching algorithm. The matching algorithm uses a distance transform to serach for local best fits between the edges of a template and edges in the underlying image. It is embedded in a resolution pyramid to decrease the risk of getting stuck in false local minima. This method makes it possible to find proteins attached to other proteins, or proteins appearing as split into parts in the images. It also decreases the amount of human interaction m´needed for identifying individual proteins of the searched kind. The method is demonstrated on a set of three volume images of the antibody IgG in solution.

  • 38.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Weighted distance transforms for images using elongated voxel grids2002Conference paper (Refereed)
    Abstract [en]

    In this paper we investigate weighted distance transforms in 3D images using elongated voxel grids. We use a local neighbourhood of size 3x3x3 and assume a voxel grid with equal resolution along two axes and lower along the third. The weights (local dista

  • 39.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Weighted distance transforms for volume images digitized in elongated voxel grids2004In: Pattern Recognition Letters, Vol. 25, p. 571-580Article in journal (Refereed)
    Abstract [en]

    Weighted distance transforms in volume (3D) images using a voxel grid with equal resolution along two axes

    and lower, one, along the third are investigated. The weights (neighbour distances) in a local neighbourhoo

    d of size 3 x 3 x 3 are optimized by minimizing the maximum error in a cubic image.

  • 40.
    Sintorn, Ida-Maria
    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.
    Gedda, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Svensson, Stina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Mata, Susana
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Medial grey-level based representation for proteins in volume images2005In: Pattern Recognition and Image Analysis: Second Iberian Conference (IbPRIA 2005), Proceedings, Part II, 2005, p. 421-428Conference paper (Refereed)
    Abstract [en]

    We present an algorithm to extract a medial representation of proteins in volume images. The representation (MGR) takes into account the internal grey-level distribution of the protein and can be extracted without first segmenting the image into object and background. We show how MGR can facilitate the analysis of the structure of the proteins and thereby also classification. Results are shown on two types of protein images.

  • 41.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Homman, Mohammed
    Description, Segmentation and Classification of Human Cytomegalovirus Capsids2002In: Proceedings SSAB'02 Symposium on Image Analysis, 2002, p. 21-24Conference paper (Other scientific)
    Abstract [en]

    Three stages in the capsid assembly of Human Cytomegalovirus in the host cell nucleus are investigated. The classes are described by a radial grey-level profile constructed for each class. A segmentation and classification method, based on matching of tem

  • 42.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Homman-Loudiyi, Mohammed
    Söderberg-Nauclér, Cecilia
    Borgefors, Gunilla
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images2004In: Computer Methods and Programs in Biomedicine, Vol. 76, p. 95-102Article in journal (Refereed)
    Abstract [en]

    An automatic image analysis method for describing, segmenting, and classifying Human Cyto\-megalo\-virus c

    apsids in transmission electron micrograph (TEM) images of host cell nuclei has been developed. Three stage

    s of the capsid assembly process in the host cell nucleus have been investigated. Each class is described b

    y a radial density profile, which is the average grey-level at each radial distance from the centre. A temp

    late, constructed from the profile, is used to find possible capsid locations by correlation based matching

    . The matching results are further refined by size and distortion analysis of each possible capsid, resulti

    ng in a final segmentation and classification.

  • 43.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kylberg, Gustaf
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Regional Zernike Moments for Texture Recognition2012In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, p. 1635-1638Conference paper (Refereed)
    Abstract [en]

     Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick co-occurrence matrices and local binary patterns on two different texture image sets, and show that they are noise insensitive and very well suited for texture recognition.

  • 44.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kylberg, Gustaf
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Virus recognition based on local texture2014In: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, p. 3227-3232Conference paper (Refereed)
    Abstract [en]

    To detect and identify viruses in electron microscopy images is crucial in certain clinical emergency situations. It is currently a highly manual task, requiring an expert sittingat the microscope to perform the analysis visually. Here wefocus on and investigate one aspect towards automating the virusdiagnostic task, namely recognizing the virus type based on theirtexture once possible virus objects have been segmented. Weshow that by using only local texture descriptors we achievea classification rate of almost 89% on texture patches from 15different virus types and a debris (false object) class. We compareand combine 5 different types of local texture descriptors andshow that by combining the different types a lower classificationerror is achieved. We use a Random Forest Classifier and comparetwo approaches for feature selection.

  • 45.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kylberg, Gustaf
    Vironova AB.
    Haag, Lars
    Vironova AB.
    Nordström, Rickard
    Vironova AB.
    Automated Image Acquisition and Particle Size Distribution in the MiniTEM Instrument2015Conference paper (Refereed)
    Abstract [en]

    The MiniTEM instrument is a desktop-top low-voltage easy to use TEM that was introduced last year. It has a high degree of automation in the microscope alignment, image acquisition, and analysis process.

    The microscope runs at 25 keV, which enables imaging of biological (negative stain and tissue sections) as well as inorganic samples prepared with standard methods. It is small, robust, requires only one standard wall socket, and can be hosted in any lab or office. The GUI is developed for Windows 8, and designed for a touch screen, allowing convenient search through the sample with pinch-zooming (changing magnification). The instrument has an integrated image processing and analysis library, which allows the user to design and apply analysis scripts. A graph based interface is used to create scripts which can be saved for future use and applied to multiple images, either acquired on the fly or manually acquired and stored in a folder.

    Here, we show how the MiniTEM instrument can be used to extract a user independent size distribution of particles in a sample in a highly automated manner. We designed analysis scripts for automatic image acquisition followed by segmentation and extraction of characteristic measures of individual particles. As a final step, obvious false-positives are manually removed which simultaneously updates the extracted measures. In the first example we image (at 1.46 nm/pixel) and analyze influenza viral vectors kindly provided by Max Planck Institute for Dynamics of Complex Technical Systems. The resulting size distribution was compared to and does agree with the distribution derived from corresponding analysis in high-voltage images (Tecnai G2 Spirit, 1.85 nm/pixel, 100 keV), see fig. 1. In the second example, we investigate the size distribution of mixtures of two differently sized polystyrene spheres (at 1.46 nm/pixel). The measured distribution (fig. 2) again coincides with the expected

  • 46.
    Sintorn, Ida-Maria
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Mata, Susana
    Using grey-level and shape information for decomposing proteins in 3D images2004In: IEEE International, 2004, p. 800-803Conference paper (Refereed)
    Abstract [en]

    An image analysis method for decomposing 3D objects using a combination

    of grey-level and shape is presented. The method consists of two major p

    arts: seeding based on grey-level information and growth from the seeds

    based on shape information. The growth is performed in two steps in orde

    r to prevent seeds located in peripheral or protruding parts of the obje

    ct from growing into other parts. The method was developed to decompose

    3D reconstructions of proteins into their structural subunits. The prote

    ins are imaged with SET (Sidec Electron Tomography) at a resolution of a

    pproximately 2nm, and delineated from the background by thresholding pri

    or to application of our decomposition method. Decomposition can be a us

    eful tool in the second step of the segmentation process to help disting

    uish between true protein molecules and other objects. It can also be us

    eful for analyzing and visualizing interactions between proteins.

  • 47.
    Suveer, Amit
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction2017In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper (Other academic)
  • 48.
    Suveer, Amit
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Dragomir, Anca
    Uppsala University Hospital.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Automated detection of cilia in low magnification transmission electron microscopy images using template matching2016In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, 2016, p. 386-390Conference paper (Other academic)
    Abstract [en]

    Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.

  • 49.
    Suveer, Amit
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Enhancement of cilia sub-structures by multiple instance registration and super-resolution reconstruction2017In: Image Analysis: Part II, Springer, 2017, p. 362-374Conference paper (Refereed)
    Abstract [en]

    Ultrastructural analysis of cilia cross-sectional images using transmission electron microscopy (TEM) assists the pathologists to diagnose Primary Ciliary Dyskinesia, a genetic disease. The current diagnostic procedure is manual and difficult because of poor signal-to-noise ratio in TEM images. In this paper, we propose an automated multi-step registration approach to register many cilia cross-sectional instances. The novelty of the work is in the utilization of customized weight masks at each registration step to achieve good alignment of the specific cilium regions. Registration is followed by super-resolution reconstruction to enhance the substructural information. Landmarks matching based evaluation of registration results in pixel alignment error of 2.35±1.82" role="presentation">2.35±1.82 pixels, and the subjective analysis of super-resolution reconstructed cilium shows a clear improvement in the visibility of the substructures such as dynein arms, radial spokes, and central pair.

  • 50.
    Svensson, Lennart
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Registration Parameter Spaces for Molecular Electron Tomography Images2011In: Image Analysis and Processing – ICIAP 2011: Part I / [ed] Maino, Giuseppe; Foresti, Gian Luca, Berlin: Springer-Verlag , 2011, p. 403-412Conference paper (Refereed)
12 1 - 50 of 58
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