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  • 1.
    Gavrilovic, Milan
    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.
    Spectral Image Processing with Applications in Biotechnology and Pathology2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans.

    Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias.

    This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing  intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations.

    The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.

    List of papers
    1. Quantification of colocalization and cross-talk based on spectral angles
    Open this publication in new window or tab >>Quantification of colocalization and cross-talk based on spectral angles
    2009 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 234, no 3, p. 311-324Article in journal (Refereed) Published
    Abstract [en]

    Common methods for quantification of colocalization in fluorescence microscopy typically require cross-talk free images or images where cross-talk has been eliminated by image processing, as they are based on intensity thresholding. Quantification of colocalization includes not only calculating a global measure of the degree of colocalization within an image, but also a classification of each image pixel as showing colocalized signals or not. In this paper, we present a novel, automated method for quantification of colocalization and classification of image pixels. The method, referred to as SpecDec, is based on an algorithm for spectral decomposition of multispectral data borrowed from the field of remote sensing. Pixels are classified based on hue rather than intensity. The hue distribution is presented as a histogram created by a series of steps that compensate for the quantization noise always present in digital image data, and classification rules are thereafter based on the shape of the angle histogram. Detection of colocalized signals is thus only dependent on the hue, making it possible to classify also low-intensity objects, and decoupling image segmentation from detection of colocalization. Cross-talk will show up as shifts of the peaks of the histogram, and thus a shift of the classification rules, making the method essentially insensitive to cross-talk. The method can also be used to quantify and compensate for cross-talk, independent of the microscope hardware.

    Place, publisher, year, edition, pages
    Oxford, UK: Blackwell Publishing, 2009
    Keywords
    Colocalization, cross-talk, fluorescence microscopy, image analysis
    National Category
    Computer and Information Sciences
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-111376 (URN)10.1111/j.1365-2818.2009.03170.x (DOI)000266180400011 ()19493110 (PubMedID)
    Projects
    EU-Strep project ENLIGHT (ENhanced LIGase based Histochemical Techniques)
    Available from: 2009-12-15 Created: 2009-12-11 Last updated: 2022-01-28Bibliographically approved
    2. Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles
    Open this publication in new window or tab >>Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles
    2009 (English)In: Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (OPTIMHisE): A satellite workshop associated with MICCAI / [ed] Daniel Elson and Nasir Rajpoot, London, 2009, p. 135-146Conference paper, Published paper (Refereed)
    Abstract [en]

    Background fluorescence, also known as autofluorescence, and cross-talk are two problems in fluorescence microscopy that stem from similar phenomena. When biological specimens are imaged, the detected signal often contains contributions from fluorescence originating from sources other than the imaged fluorophore. This fluorescence could either come from the specimen itself (autofluorescence), or from fluorophores with partly overlapping emission spectra (cross-talk). In order to resolve spectral components at least two distinct wavelength intervals have to be imaged. This paper shows how autofluorescence can be presented statistically using a spectral angle histogram. Pixel classification by spectral angles was previously developed for detection and quantification of colocalization. Here we show how the spectral angle histogram can be employed to suppress autofluorescence. First, classical background subtraction (also referred to as linear unmixing) is presented in the form of a fuzzy classification by spectral angles. A modification of the fuzzy classification rules is also presented and we show that sigmoid membership functions lead to better suppression of background and amplification of true signals.

    Place, publisher, year, edition, pages
    London: , 2009
    Keywords
    autofluorescence, fluorescence microscopy, multispectral image analysis, fuzzy classification, dimensionality reduction
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-111374 (URN)978-0-9563776-0-9 (ISBN)
    Conference
    MICCAI 2009, the 12th International Conference on Medical Image Computing and Computer Assisted Intervention
    Projects
    EU-Strep project ENLIGHT (ENhanced LIGase based Histochemical Techniques)
    Available from: 2009-12-16 Created: 2009-12-11 Last updated: 2022-01-28Bibliographically approved
    3. Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy
    Open this publication in new window or tab >>Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy
    Show others...
    2011 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 79A, no 7, p. 518-527Article in journal (Refereed) Published
    Abstract [en]

    Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.

    National Category
    Cell and Molecular Biology Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-156962 (URN)10.1002/cyto.a.21087 (DOI)000292947900004 ()
    Available from: 2011-08-20 Created: 2011-08-11 Last updated: 2022-01-28Bibliographically approved
    4. Blind Color Decomposition of Histological Images
    Open this publication in new window or tab >>Blind Color Decomposition of Histological Images
    Show others...
    2013 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, no 6, p. 983-994Article in journal (Refereed) Published
    Abstract [en]

    Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.

    National Category
    Medical Image Processing
    Research subject
    Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-160312 (URN)10.1109/TMI.2013.2239655 (DOI)000319701800002 ()
    Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2022-01-28
    Download full text (pdf)
    fulltext
  • 2.
    Gavrilovic, Milan
    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.
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Conze, Tim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. 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, 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.
    Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy2011In: Cytometry Part A, ISSN 1552-4922, Vol. 79A, no 7, p. 518-527Article in journal (Refereed)
    Abstract [en]

    Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.

  • 3.
    Gavrilovic, Milan
    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.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for cross-talk suppression in fluorescence microscopy2008In: Medicinteknikdagarna 2008, 2008, p. 64-64Conference paper (Other academic)
    Abstract [en]

     

     

     

    Download full text (pdf)
    FULLTEXT01
  • 4.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Spectral Angle Histogram: a Novel Image Analysis Tool for Quantification of Colocalization and Cross-talk2009In: 9th International ELMI Meeting on Advanced Light Microscopy / [ed] Kurt Anderson, Gail McConnell, Glasgow, UK, 2009, p. 66-67Conference paper (Other academic)
    Abstract [en]

    In fluorescence microscopy, when analyzing spectral components, it is common to record two (or more) greyscale images. Each greyscale image, referred to as a channel, corresponds to intensities in different wavelength intervals. If each pixel of a two-channel image is plotted in a space spanned by the two intensity channels a conventional scatter-plot is obtained. Single-coloured pixels are distributed along the axes, while colocalized pixels are distributed closer to the diagonal of the scatter-plot, and cross-talk (as well as noise) is observed as deviations of the single-coloured vectors from the axes. Detection of colocalized pixels is often based on a division of this 2D space into different regions by intensity thresholding. We have developed a method for reducing the scatter-plot to a 1D spectral angle histogram through a series of steps that compensate for the quantization noise which is always present in digital image data.

    Using the spectral angle histogram, we can quantify colocalization in a fully automated and robust manner. As compared to previous methods for quantification of colocalization, this approach is insensitive to cross-talk. In fact, it can also be employed to quantify and compensate for cross-talk, using either linear unmixing or fuzzy classification by spectral angle, ensuring complete suppression of cross-talk with minimal loss of information. Recently we started investigating how the method can deal with autofluorescence. Initial tests on real image data show that the method may be useful for improved background suppression and amplification of the true signals.

    The article “Quantification of colocalization and cross-talk based on spectral angles”, describing the method, is about to be published in the Journal of Microscopy. Authors have also filed a patent application “Pixel classification in image analysis” in 2008.

    Download full text (txt)
    ELMIabstract
  • 5.
    Weibrecht, Irene
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gavrilovic, Milan
    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.
    Lindbom, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. 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, 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.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Visualising individual sequence-specific protein-DNA interactions in situ2012In: New Biotechnology, ISSN 1871-6784, E-ISSN 1876-4347, Vol. 29, no 5, p. 589-598Article in journal (Refereed)
    Abstract [en]

    Gene expression-a key feature for modulating cell fate-is regulated in part by histone modifications, which modulate accessibility of the chromatin to transcription factors. Until now, protein-DNA interactions (PDIs) have mostly been studied in bulk without retrieving spatial information from the sample or with poor sequence resolution. New tools are needed to reveal proteins interacting with specific DNA sequences in situ for further understanding of the orchestration of transcriptional control within the nucleus. We present herein an approach to visualise individual PDIs within cells, based on the in situ proximity ligation assay (PLA). This assay, previously used for the detection of protein-protein interactions in situ, was adapted for analysis of target PDIs, using padlock probes to identify unique DNA sequences in complex genomes. As a proof-of-principle we detected histone H3 interacting with a 26bp consensus sequence of the Alu-repeat abundantly expressed in the human genome, but absent in mice. However, the mouse genome contains a highly similar sequence, providing a model system to analyse the selectivity of the developed methods. Although efficiency of detection currently is limiting, we conclude that in situ PLA can be used to achieve a highly selective analysis of PDIs in single cells.

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