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  • 1.
    Aiesh, Basel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis.
    Measurement of dispersion barriers through SEM images2015Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis digital image analysis is applied to Scanning Electron Microscope imagesof dispersion barriers to measure specific properties. The thin barriers are used asprotection for paperboard packaging and are made of polymers and fillers. The orientation, area, length and density distributions of the fillers determine the functionality and quality of the barrier. Methods built on image analysis tools are developed with the objective to measure these quantities. Input for the methods are Scanning Electron Microscope images showing the cross-section of the barriers. To make the images relevant for the methods they are preprocessed by reducing noise and distinguishing fillers from the background.

    For measuring the orientation distribution of the fillers two different methods are implemented and compared. The first one is based on a structure tensor and the other one applies a covariance matrix. The structure tensor is preferable because of its flexibility and better performance for complex images. The area and length distributions are measured by applying mathematical morphology together withsoft-clipping. The density distribution is obtained by filtering the underlying image twice with a uniform filter which creates a heat map.

    The developed methods are evaluated by applying them on fabricated binary test images with known properties. The methods are very accurate when applied on simple test images but for more complex test images with greater variation the accuracy decreases. However, for most applications the results are still on an acceptable level.

  • 2.
    Allalou, Amin
    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.
    Methods for 2D and 3D Quantitative Microscopy of Biological Samples2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening. 

    It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that  produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions. 

    HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.

    List of papers
    1. A detailed analysis of 3D subcellular signal localization
    Open this publication in new window or tab >>A detailed analysis of 3D subcellular signal localization
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    2009 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 75A, no 4, p. 319-328Article in journal (Refereed) Published
    Abstract [en]

    Detection and localization of fluorescent signals in relation to other subcellular structures is an important task in various biological studies. Many methods for analysis of fluorescence microscopy image data are limited to 2D. As cells are in fact 3D structures, there is a growing need for robust methods for analysis of 3D data. This article presents an approach for detecting point-like fluorescent signals and analyzing their subnuclear position. Cell nuclei are delineated using marker-controlled (seeded) 3D watershed segmentation. User-defined object and background seeds are given as input, and gradient information defines merging and splitting criteria. Point-like signals are detected using a modified stable wave detector and localized in relation to the nuclear membrane using distance shells. The method was applied to a set of biological data studying the localization of Smad2-Smad4 protein complexes in relation to the nuclear membrane. Smad complexes appear as early as 1 min after stimulation while the highest signal concentration is observed 45 min after stimulation, followed by a concentration decrease. The robust 3D signal detection and concentration measures obtained using the proposed method agree with previous observations while also revealing new information regarding the complex formation.

    Keywords
    3D image analysis, fluorescence signal segmentation, subcellular positioning, Smad detection
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-98014 (URN)10.1002/cyto.a.20663 (DOI)000264513800006 ()
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2018-01-13Bibliographically approved
    2. Single-cell A3243G mitochondrial DNA mutation load assays for segregation analysis
    Open this publication in new window or tab >>Single-cell A3243G mitochondrial DNA mutation load assays for segregation analysis
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    2007 (English)In: Journal of Histochemistry and Cytochemistry, ISSN 0022-1554, E-ISSN 1551-5044, Vol. 55, no 11, p. 1159-1166Article in journal (Refereed) Published
    Abstract [en]

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

    Keywords
    A3243G mtDNA, Aging, Heteroplasmy, Mitochondrial diseases, Mutation load, Padlock probing, PCR-RFLP, Segregation
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-12658 (URN)10.1369/jhc.7A7282.2007 (DOI)000250320100009 ()17679731 (PubMedID)
    Available from: 2008-01-09 Created: 2008-01-09 Last updated: 2017-12-11Bibliographically approved
    3. BlobFinder, a tool for fluorescence microscopy image cytometry
    Open this publication in new window or tab >>BlobFinder, a tool for fluorescence microscopy image cytometry
    2009 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 94, no 1, p. 58-65Article in journal (Refereed) Published
    Abstract [en]

    Images can be acquired at high rates with modern fluorescence microscopy hardware, giving rise to a demand for high-speed analysis of image data. Digital image cytometry, i.e., automated measurements and extraction of quantitative data from images of cells, provides valuable information for many types of biomedical analysis. There exists a number of different image analysis software packages that can be programmed to perform a wide array of useful measurements. However, the multi-application capability often compromises the simplicity of the tool. Also, the gain in speed of analysis is often compromised by time spent learning complicated software. We provide a free software called BlobFinder that is intended for a limited type of application, making it easy to use, easy to learn and optimized for its particular task. BlobFinder can perform batch processing of image data and quantify as well as localize cells and point like source signals in fluorescence microscopy images, e.g., from FISH, in situ PLA and padlock probing, in a fast and easy way.

    Keywords
    Image cytometry, Single cell analysis, FISH, Software
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-87971 (URN)10.1016/j.cmpb.2008.08.006 (DOI)000264282400006 ()18950895 (PubMedID)
    Available from: 2009-01-22 Created: 2009-01-16 Last updated: 2018-06-26Bibliographically approved
    4. Robust signal detection in 3D fluorescence microscopy
    Open this publication in new window or tab >>Robust signal detection in 3D fluorescence microscopy
    2010 (English)In: Cytometry. Part A, ISSN 1552-4922, Vol. 77A, no 1, p. 86-96Article in journal (Refereed) Published
    Abstract [en]

    Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to Visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data.

    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:uu:diva-98015 (URN)10.1002/cyto.a.20795 (DOI)000273384700011 ()
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2011-11-04Bibliographically approved
    5. High-throughput in vivo optical projection tomography of small vertebrates
    Open this publication in new window or tab >>High-throughput in vivo optical projection tomography of small vertebrates
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    (English)Manuscript (preprint) (Other academic)
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:uu:diva-159203 (URN)
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2011-11-04
    6. Fully automated cellular-resolution vertebrate screening platform with parallel animal processing
    Open this publication in new window or tab >>Fully automated cellular-resolution vertebrate screening platform with parallel animal processing
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    2012 (English)In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 12, no 4, p. 711-716Article in journal (Refereed) Published
    Abstract [en]

    The zebrafish larva is an optically-transparent vertebrate model with complex organs that is widelyused to study genetics, developmental biology, and to model various human diseases. In this article, wepresent a set of novel technologies that significantly increase the throughput and capabilities of ourpreviously described vertebrate automated screening technology (VAST). We developed a robustmulti-thread system that can simultaneously process multiple animals. System throughput is limitedonly by the image acquisition speed rather than by the fluidic or mechanical processes. We developedimage recognition algorithms that fully automate manipulation of animals, including orienting andpositioning regions of interest within the microscope’s field of view. We also identified the optimalcapillary materials for high-resolution, distortion-free, low-background imaging of zebrafish larvae.

    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-159202 (URN)10.1039/c1lc20849g (DOI)000299380800007 ()
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2018-01-12Bibliographically approved
    7. Image based measurements of single cell mtDNA mutation load MTD 2007
    Open this publication in new window or tab >>Image based measurements of single cell mtDNA mutation load MTD 2007
    Show others...
    2007 (English)In: Medicinteknikdagarna 2007, 2007Conference paper, Published paper (Other (popular science, discussion, etc.))
    Abstract [en]

    Cell cultures as well as cells in tissue always display a certain degree of variability,and measurements based on cell averages will miss important information contained in a heterogeneous population. These differences among cells in a population may be essential to quantify when looking at, e.g., protein expression and mutations in tumor cells which often show high degree of heterogeneity.

    Single nucleotide mutations in the mithochondrial DNA (mtDNA) can accumulate and later be present in large proportions of the mithocondria causing devastating diseases. To study mtDNA accumulation and segregation one needs to measure the amount of mtDNA mutations in each cell in multiple serial cell culture passages. The different degrees of mutation in a cell culture can be quantified by making measurements on individual cells as an alternative to looking at an average of a population. Fluorescence microscopy in combination with automated digital image analysis provides an efficient approach to this type of single cell analysis.

    Image analysis software for these types of applications are often complicated and not easy to use for persons lacking extensive knowledge in image analysis, e.g., laboratory personnel. This paper presents a user friendly implementation of an automated method for image based measurements of mtDNA mutations in individual cells detected with padlock probes and rolling-circle amplification (RCA). The mitochondria are present in the cell’s cytoplasm, and here each cytoplasm has to be delineated without the presence of a cytoplasmic stain. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%. The final image based measurements of mitochondrial mutation load are also compared to, and show high agreement with, measurements made using biochemical techniques.

    National Category
    Other Computer and Information Science
    Identifiers
    urn:nbn:se:uu:diva-12745 (URN)
    Available from: 2008-01-11 Created: 2008-01-11 Last updated: 2018-01-12Bibliographically approved
    8. Increasing the dynamic range of in situ PLA
    Open this publication in new window or tab >>Increasing the dynamic range of in situ PLA
    Show others...
    2011 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 8, no 11, p. 892-893Article in journal, Editorial material (Refereed) Published
    National Category
    Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-159199 (URN)10.1038/nmeth.1743 (DOI)000296891800004 ()
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2017-12-08Bibliographically approved
    9. High-throughput cellular-resolution in vivo vertebrate screening
    Open this publication in new window or tab >>High-throughput cellular-resolution in vivo vertebrate screening
    Show others...
    2011 (English)In: Proc. 15th International Conference on Miniaturized Systems for Chemistry and Life Sciences, 2011Conference paper, Published paper (Refereed)
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-159201 (URN)
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2011-11-04
  • 3.
    Allalou, Amin
    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.
    Curic, Vladimir
    Pardo-Martin, Carlos
    Massachusetts Institute of Technology, USA.
    Yanik, Mehmet Fatih
    Massachusetts Institute of Technology, USA.
    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.
    Approaches for increasing throughput andinformation content of image-based zebrafishscreens2011In: Proceeding of SSBA 2011, 2011Conference paper (Other academic)
    Abstract [en]

    Microscopy in combination with image analysis has emerged as one of the most powerful and informativeways to analyze cell-based high-throughput screening (HTS) samples in experiments designed to uncover novel drugs and drug targets. However, many diseases and biological pathways can be better studied in whole animals, particularly diseases and pathways that involve organ systems and multicellular interactions, such as organ development, neuronal degeneration and regeneration, cancer metastasis, infectious disease progression and pathogenesis. The zebrafish is a wide-spread and popular vertebrate model of human organfunction and development, and it is unique in the sense that large-scale in vivo genetic and chemical studies are feasible due in part to its small size, optical transparency,and aquatic habitat. To improve the throughput and complexity of zebrafish screens, a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae has been developed at Yanik lab at Research Laboratory of Electronics, MIT, USA. The system loads live zebrafish from reservoirs or multiwell plates, positions and rotates them for high-speed confocal imaging of organs,and dispenses the animals without damage. We present two improvements to the described system, including automation of positioning of the animals and a novel approach for brightfield microscopy tomographic imaging of living animals.

  • 4.
    Bajic, Buda
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    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.
    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.
    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, Centre for Image Analysis. 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, 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. 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, Centre for Image Analysis. 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, 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, Centre for Image Analysis. 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, 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. 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. Vironova AB, Stockholm, Sweden.
    Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement2018In: International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, p. 921-925Conference paper (Refereed)
  • 5.
    Blom, Elisabeth
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Biochemistry and Organic Chemistry.
    Velikyan, Irina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Biomedical Radiation Sciences.
    Monazzam, Azita
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Endocrine Tumor Biology.
    Razifar, Pasha
    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.
    Nair, Manoj
    Razifar, Payam
    Vanderheyden, Jean-Luc
    Krivoshein, Arcadius V.
    Backer, Marina
    Backer, Joseph
    Långström, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Biochemistry and Organic Chemistry.
    Synthesis and characterization of scVEGF-PEG-[68Ga]NOTA and scVEGF-PEG-[68Ga]DOTA PET tracers2011In: Journal of labelled compounds & radiopharmaceuticals, ISSN 0362-4803, E-ISSN 1099-1344, Vol. 54, no 11, p. 685-692Article in journal (Refereed)
    Abstract [en]

    Vascular endothelial growth factor (VEGF) signaling via vascular endothelial growth factor receptor 2 (VEGFR-2) on tumor endothelial cells is a critical driver of tumor angiogenesis. Novel anti-angiogenic drugs target VEGF/VEGFR-2 signaling and induce changes in VEGFR-2 prevalence. To monitor VEGFR-2 prevalence in the course of treatment, we are evaluating (68)Ga positron emission tomography imaging agents based on macrocyclic chelators, site-specifically conjugated via polyethylene glycol (PEG) linkers to engineered VEGFR-2 ligand, single-chain (sc) VEGF. The (68)Ga-labeling was performed at room temperature with NOTA (2,2', 2 ''-(1,4,7-triazonane-1,4,7-triyl) triacetic acid) conjugates or at 90 degrees C by using either conventional or microwave heating with NOTA and DOTA (2,2', 2 '', 2'''-(1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl) tetraacetic acid) conjugates. The fastest (similar to 2min) and the highest incorporation (>90%) of (68)Ga into conjugate that resulted in the highest specific radioactivity (similar to 400MBq/nmol) was obtained with microwave heating of the conjugates. The bioactivity of the NOTA-and DOTA-containing tracers was validated in 3-D tissue culture model of 293/KDR cells engineered to express high levels of VEGFR-2. The NOTA-containing tracer also displayed a rapid accumulation (similar to 20s after intravenous injection) to steady-state level in xenograft tumor models. A combination of high specific radioactivity and maintenance of functional activity suggests that scVEGF-PEG-[(68)Ga] NOTA and scVEGF-PEG-[(68)Ga] DOTA might be promising tracers for monitoring VEGFR-2 prevalence and should be further explored.

  • 6.
    Byström, Anna
    et al.
    Swedish University of Agricultural Sciences, Department of Anatomy, Physiology and Biochemistry.
    Roepstorff, Lars
    Swedish University of Agricultural Sciences, Department of Anatomy, Physiology and Biochemistry.
    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.
    Image Analysis of Saddle Pressure Data2011Conference paper (Other academic)
  • 7.
    Chinga-Carrasco, Gary
    et al.
    Paper and Fibre Research Institute (PFI), Norway.
    Miettinen, Arttu
    Department of Physics, University of Jyväskylä, Finland.
    Luengo Hendriks, Cris L.
    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.
    Gamstedt, E. Kristofer
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics.
    Kataja, Markku
    Department of Physics, University of Jyväskylä, Finland.
    Structural Characterisation of Kraft Pulp Fibres and Their Nanofibrillated Materials for Biodegradable Composite Applications2011In: Nanocomposites and Polymers with Analytical Methods / [ed] Cuppoletti, John, InTech , 2011, p. 243-260Chapter in book (Refereed)
  • 8.
    Clausson, Carl-Magnus
    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.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Science for Life Laboratory, SciLifeLab.
    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.
    Mahmoudi, Salah
    Farnebo, Marianne
    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, 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. 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.
    Increasing the dynamic range of in situ PLA2011In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 8, no 11, p. 892-893Article in journal (Refereed)
  • 9.
    Curic, Vladimir
    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.
    Lindblad, Joakim
    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, Centre for Image Analysis. 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. Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Distance measures between digital fuzzy objects and their applicability in image processing2011In: Combinatorial Image Analysis / [ed] Jake Aggarwal, Reneta Barneva, Valentin Brimkov, Kostadin Koroutchev, Elka Koroutcheva, Springer Berlin/Heidelberg, 2011, Vol. 6636, p. 385-397Conference paper (Refereed)
    Abstract [en]

    We present two different extensions of the Sum of minimal distances and the Complement weighted sum of minimal distances to distances between fuzzy sets. We evaluate to what extent the proposed distances show monotonic behavior with respect to increasing translation and rotation of digital objects, in noise free, as well as in noisy conditions. Tests show that one of the extension approaches leads to distances exhibiting very good performance. Furthermore, we evaluate distance based classification of crisp and fuzzy representations of objects at a range of resolutions. We conclude that the proposed distances are able to utilize the additional information available in a fuzzy representation, thereby leading to improved performance of related image processing tasks.

  • 10.
    Dražić, Slobodan
    et al.
    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, 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. 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. Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Precise Estimation of the Projection of a Shape from a Pixel Coverage Representation2011In: Proceedings of the 7th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE Computer Society, 2011, p. 569-574Conference paper (Refereed)
    Abstract [en]

    Measuring width and diameter of a shape areproblems well studied in the literature. A pixel coverage repre-sentation is one specific type of digital fuzzy representation of acontinuous image object, where the (membership) value of eachpixel is (approximately) equal to the relative area of the pixelwhich is covered by the continuous object. Lately a number ofmethods for shape analysis use pixel coverage for reducing errorof estimation. We introduce a novel method for estimating theprojection of a shape in a given direction. The method is based onutilizing pixel coverage representation of a shape. Performance ofthe method is evaluated by a number of tests on synthetic objects,confirming high precision and applicability for calculation ofdiameter and elongation of a shape.

  • 11.
    Fowlkes, Charless C.
    et al.
    Department of Computer Science, University of California Irvine.
    Eckenrode, Kelly B.
    Department of Systems Biology, Harvard Medical School.
    Bragdon, Meghan D.
    Department of Systems Biology, Harvard Medical School.
    Meyer, Miriah
    School of Engineering and Applied Sciences, Harvard University.
    Wunderlich, Zeba
    Department of Systems Biology, Harvard Medical School.
    Simirenko, Lisa
    California Institute for Quantitative Biosciences, University of California Berkeley.
    Luengo Hendriks, Cris L.
    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.
    Keränen, Soile V. E.
    Genomics and Life Sciences Division, Lawrence Berkeley National Laboratory.
    Henriquez, Clara
    Genomics and Life Sciences Division, Lawrence Berkeley National Laboratory.
    Knowles, David W.
    Genomics and Life Sciences Division, Lawrence Berkeley National Laboratory.
    Biggin, Mark D.
    Genomics and Life Sciences Division, Lawrence Berkeley National Laboratory.
    Eisen, Michael B.
    California Institute for Quantitative Biosciences, University of California Berkeley.
    DePace, Angela H.
    Department of Systems Biology, Harvard Medical School.
    A Conserved Developmental Patterning Network Produces Quantitatively Different Output in Multiple Species of Drosophila2011In: PLoS Genetics, ISSN 1553-7390, Vol. 7, no 10, p. e1002346-Article in journal (Refereed)
  • 12.
    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: 2018-01-12Bibliographically 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: 2018-01-12Bibliographically 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: 2018-01-12Bibliographically 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: 2018-12-02
  • 13.
    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.
    Jimmy, Azar
    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, Centre for Image Analysis.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Carlbom, Ingrid
    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, Centre for Image Analysis.
    Tissue Separation for Quantitative Malignancy Grading of Prostate Cancer2011In: Abstracts of Medicinteknikdagarna 2011, 2011, p. 32-32Conference paper (Other academic)
  • 14.
    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.

  • 15.
    Gustavson, Stefan
    et al.
    Department of Science and Technology, Linköping University.
    Strand, Robin
    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.
    Anti-aliased Euclidean distance transform2011In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 32, no 2, p. 252-257Article in journal (Refereed)
    Abstract [en]

    We present a modified distance measure for use with distance transforms of anti-aliased, area sampled grayscale images of arbitrary binary contours. The modified measure can be used in any vector-propagation Euclidean distance transform. Our test implementation in the traditional SSED8 algorithm shows a considerable improvement in accuracy and homogeneity of the distance field compared to a traditional binary image transform. At the expense of a 10× slowdown for a particular image resolution, we achieve an accuracy comparable to a binary transform on a supersampled image with 16 × 16 higher resolution, which would require 256 times more computations and memory.

  • 16.
    Hammarqvist, Ulf
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis.
    Audio editing in the time-frequency domain using the Gabor Wavelet Transform2011Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Visualization, processing and editing of audio, directly on a time-frequency surface, is the scope of this thesis. More precisely the scalogram produced by a Gabor Wavelet transform is used, which is a powerful alternative to traditional techinques where the wave form is the main visual aid and editting is performed by parametric filters. Reconstruction properties, scalogram design and enhancements as well audio manipulation algorithms are investigated for this audio representation.The scalogram is designed to allow a flexible choice of time-frequency ratio, while maintaining high quality reconstruction. For this mean, the Loglet is used, which is observed to be the most suitable filter choice.  Re-assignmentare tested, and a novel weighting function using partial derivatives of phase is proposed.  An audio interpolation procedure is developed and shown to perform well in listening tests.The feasibility to use the transform coefficients directly for various purposes is investigated. It is concluded that Pitch shifts are hard to describe in the framework while noise thresh holding works well. A downsampling scheme is suggested that saves on operations and memory consumption as well as it speeds up real world implementations significantly. Finally, a Scalogram 'compression' procedure is developed, allowing the caching of an approximate scalogram.

  • 17. Ibrahim, Muhammad Talal
    et al.
    Niazi, M. Khalid Khan
    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.
    Guan, Ling
    Horizontal features based illumination normalization method for face recognition2011Conference paper (Refereed)
  • 18.
    Kylberg, Gustaf
    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.
    Kylberg Texture Dataset v. 1.02011Report (Other academic)
  • 19.
    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)
  • 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.
    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.

  • 21.
    Luengo Hendriks, Cris L.
    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.
    Malm, Patrik
    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.
    Bengtsson, Ewert
    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.
    Rapid prototyping of image analysis applications2011In: Medical Image Processing: Techniques and Applications / [ed] G. Dougherty, Springer , 2011, p. 5-25Chapter in book (Refereed)
  • 22. Maddah, Farzaneh
    et al.
    Soeria-Atmadja, Daniel
    Malm, Patrik
    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.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Hammerling, Ulf
    Interrogating health-related public databases from a food toxicology perspective: Computational analysis of scoring data2011In: Food and Chemical Toxicology, ISSN 0278-6915, E-ISSN 1873-6351, Vol. 49, no 11, p. 2830-2840Article in journal (Refereed)
    Abstract [en]

    Over the last 15 years, an expanding number of databases with information on noxious effects of substances on mammalian organisms and the environment have been made available on the Internet. This set of databases is a key source of information for risk assessment within several areas of toxicology. Here we present features and relationships across a relatively wide set of publicly accessible databases broadly within toxicology, in part by clustering multi-score representations of such repositories, to support risk assessment within food toxicology. For this purpose 36 databases were each scrutinized, using 18 test substances from six different categories as probes. Results have been analyzed by means of various uni- and multi-variate statistical operations. The former included a special index devised to afford context-specific rating of databases across a highly heterogeneous data matrix, whereas the latter involved cluster analysis, enabling the identification of database assemblies with overall shared characteristics. One database – HSDB – was outstanding due to rich and qualified information for most test substances, but an appreciable fraction of the interrogated repositories showed good to decent scoring. Among the six chosen substance groups, Food contact materials had the most comprehensive toxicological information, followed by the Pesticides category.

  • 23.
    Malm, Patrik
    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.
    Bengtsson, Ewert
    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.
    The CerviSCAN project: Project description and current progress2011In: Proceedings SSBA 2011, 2011Conference paper (Other academic)
    Abstract [en]

    Cervical cancer is the second most common type of cancer among women in spite of the fact that it through screening easily can be detected and cured before it becomes invasive. Current screening procedures are too complex and costly for use in developing countries. TheCerviSCAN project is an attempt to create a automated cervical cancer screening system that will lower the cost and increase the throughput of samples. This paper accounts for the current progress of the project as well as some of the planned future work.

  • 24.
    Malmberg, Filip
    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.
    Graph-based Methods for Interactive Image Segmentation2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The subject of digital image analysis deals with extracting relevant information from image data, stored in digital form in a computer. A fundamental problem in image analysis is image segmentation, i.e., the identification and separation of relevant objects and structures in an image. Accurate segmentation of objects of interest is often required before further processing and analysis can be performed.

    Despite years of active research, fully automatic segmentation of arbitrary images remains an unsolved problem. Interactive, or semi-automatic, segmentation methods use human expert knowledge as additional input, thereby making the segmentation problem more tractable. The goal of interactive segmentation methods is to minimize the required user interaction time, while maintaining tight user control to guarantee the correctness of the results. Methods for interactive segmentation typically operate under one of two paradigms for user guidance: (1) Specification of pieces of the boundary of the desired object(s). (2) Specification of correct segmentation labels for a small subset of the image elements. These types of user input are referred to as boundary constraints and regional constraints, respectively.

    This thesis concerns the development of methods for interactive segmentation, using a graph-theoretic approach. We view an image as an edge weighted graph, whose vertex set is the set of image elements, and whose edges are given by an adjacency relation among the image elements. Due to its discrete nature and mathematical simplicity, this graph based image representation lends itself well to the development of efficient, and provably correct, methods.

    The contributions in this thesis may be summarized as follows:

    • Existing graph-based methods for interactive segmentation are modified to improve their performance on images with noisy or missing data, while maintaining a low computational cost.
    • Fuzzy techniques are utilized to obtain segmentations from which feature measurements can be made with increased precision.
    • A new paradigm for user guidance, that unifies and generalizes regional and boundary constraints, is proposed.

    The practical utility of the proposed methods is illustrated with examples from the medical field.

    List of papers
    1. A 3D live-wire segmentation method for volume images using haptic interaction
    Open this publication in new window or tab >>A 3D live-wire segmentation method for volume images using haptic interaction
    2006 (English)In: DISCRETE GEOMETRY FOR COMPUTER IMAGERY, PROCEEDINGS 4245, 2006, p. 663-673Conference paper, Published paper (Refereed)
    Abstract [en]

    Designing interactive segmentation methods for digital volume images is difficult, mainly because efficient 3D interaction is much

    harder to achieve than interaction with 2D images. To overcome this issue, we use a system that combines stereo graphics and haptics to facilitate efficient 3D interaction. We propose a new method, based on the 2D live-wire method, for segmenting volume images. Our method consists of two parts: an interface for drawing 3D live-wire curves onto the boundary of an object in a volume image, and an algorithm for connecting two such curves to create a discrete surface.

    Identifiers
    urn:nbn:se:uu:diva-19901 (URN)0302-9743 (ISBN)
    Available from: 2006-12-04 Created: 2006-12-04 Last updated: 2011-05-05
    2. Minimal Cost-Path for Path-Based Distances
    Open this publication in new window or tab >>Minimal Cost-Path for Path-Based Distances
    2007 (English)In: Proceedings of 5th International Symposium on Image and Signal Processing and Analysis (ISPA 2007), 2007, p. 379-384Conference paper, Published paper (Refereed)
    Abstract [en]

    Distance functions defined by the minimal cost-path using weights and neighbourhood sequences (n.s.) are considered for the constrained distance transform (CDT). The CDT is then used to find one minimal cost-path between two points. The behaviour of some path-based distance functions is analyzed and a new error function is introduced. It is concluded that the weighted n.s.-distance with two weights (3 x 3 neighbourhood) and the weighted distance with three weights (5 x 5 neighbourhood) have similar properties in terms of minimal cost-path computation, while the former is more efficient to compute.

    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-12585 (URN)doi:10.1109/ISPA.2007.4383723 (DOI)978-953-184-116-0 (ISBN)
    Available from: 2008-01-07 Created: 2008-01-07 Last updated: 2018-01-12
    3. Sub-pixel Segmentation with the Image Foresting Transform
    Open this publication in new window or tab >>Sub-pixel Segmentation with the Image Foresting Transform
    2009 (English)In: Proceedings of International Workshop on Combinatorial Image Analysis: IWCIA 2009, Springer , 2009, p. 201-211Conference paper, Published paper (Refereed)
    Abstract [en]

    The Image Foresting Transform (IFT) is a framework forimage partitioning, commonly used for interactive segmentation. Givenan image where a subset of the image elements (seed-points) have beenassigned user-defined labels, the IFT completes the labeling by computingminimal cost paths from all image elements to the seed-points. Eachimage element is then given the same label as the closest seed-point. Inits original form, the IFT produces crisp segmentations, i.e., each imageelement is assigned the label of exactly one seed-point. Here, we proposea modified version of the IFT that computes region boundaries withsub-pixel precision by allowing mixed labels at region boundaries. Wedemonstrate that the proposed sub-pixel IFT allows properties of thesegmented object to be measured with higher precision.

    Place, publisher, year, edition, pages
    Springer, 2009
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 5852
    Keywords
    Image foresting transform, Interactive image segmentation, Sub-pixel precision
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-111261 (URN)10.1007/978-3-642-10210-3_16 (DOI)978-3-642-10208-0 (ISBN)
    Available from: 2009-12-08 Created: 2009-12-08 Last updated: 2018-12-02Bibliographically approved
    4. Relaxed Image Foresting Transforms for Interactive Volume Image Segmentation
    Open this publication in new window or tab >>Relaxed Image Foresting Transforms for Interactive Volume Image Segmentation
    Show others...
    2010 (English)In: MEDICAL IMAGING 2010: IMAGE PROCESSING / [ed] Dawant BM, Haynor DR, 2010, Vol. 7623Conference paper, Published paper (Refereed)
    Abstract [en]

    The image Foresting (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned correct segmentation labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. Here, we propose the relaxed IFT (RIFT). This modified version of the IFT features an additional parameter to control the smoothness of the segmentation boundary. The RIFT yields more intuitive segmentation results in the presence of noise and weak edges, while maintaining a low computational complexity. We show an application of the method to the refinement of manual segmentations of a thoracolumbar muscle in magnetic resonance images. The performed study shows that the refined segmentations are qualitatively similar to the manual segmentations, while intra-user variations are reduced by more than 50%.

    Series
    Proceedings of SPIE-The International Society for Optical Engineering, ISSN 0277-786X ; 7623
    Keywords
    Seeded segmentation, Interactive segmentation, Minimum cost paths, Image Foresting Transform
    National Category
    Medical and Health Sciences Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-140957 (URN)10.1117/12.840019 (DOI)000285048800137 ()978-0-8194-8024-8 (ISBN)
    Conference
    Conference on Medical Imaging 2010 - Image Processing San Diego, CA, FEB 14-16, 2010
    Available from: 2011-01-10 Created: 2011-01-10 Last updated: 2018-01-12Bibliographically approved
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