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  • 1. Bernander, Karl B.
    et al.
    Gustavsson, Kenneth
    Selig, Bettina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Improving the stochastic watershed2013In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 34, no 9, p. 993-1000Article in journal (Refereed)
    Abstract [en]

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

  • 2. Borodulina, Svetlana
    et al.
    Wernersson, Erik L. G.
    Kulachenko, Artem
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Extracting fiber and network connectivity data using microtomography images of paper2016In: Nordic Pulp & Paper Research Journal, ISSN 0283-2631, E-ISSN 2000-0669, Vol. 31, no 3, p. 469-478Article in journal (Refereed)
  • 3. Cadenas, José Oswaldo
    et al.
    Megson, Graham M.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Preconditioning 2D integer data for fast convex hull computations2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 3, article id e0149860Article in journal (Refereed)
  • 4.
    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)
  • 5.
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Hendriks Luengo, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Adaptive structuring elements based on salience information2012In: Computer Vision and Graphics / [ed] L. Bolc, K. Wojciechowski, R. Tadeusiewicz, L.J. Chmielewski, Springer, 2012, p. 321-328Conference paper (Other academic)
    Abstract [en]

    Adaptive structuring elements modify their shape and size according to the image content and may outperform fixed structuring elements. Without any restrictions, they suffer from a high computational complexity, which is often higher than linear with respect to the number of pixels in the image. This paper introduces adaptive structuring elements that have predefined shape, but where the size is adjusted to the local image structures. The size of adaptive structuring elements is determined by the salience map that corresponds to the salience of the edges in the image, which can be computed in linear time. We illustrate the difference between the new adaptive structuring elements and morphological amoebas. As an example of its usefulness, we show how the new adaptive morphological operations can isolate the text in historical documents.

  • 6.
    Curic, Vladimir
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Landström, Anders
    Thurley, Matthew J.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Adaptive Mathematical Morphology: a survey of the field2014In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 47, p. 18-28Article in journal (Refereed)
    Abstract [en]

    We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies.

  • 7.
    Curic, Vladimir
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lefèvre, Sébastien
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Adaptive hit or miss transform2015In: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer, 2015, p. 741-752Conference paper (Refereed)
    Abstract [en]

    The Hit or Miss Transform is a fundamental morphological operator, and can be used for template matching. In this paper, we present a framework for adaptive Hit or Miss Transform, where structuring elements are adaptive with respect to the input image itself. We illustrate the difference between the new adaptive Hit or Miss Transform and the classical Hit or Miss Transform. As an example of its usefulness, we show how the new adaptive Hit or Miss Transform can detect particles in single molecule imaging.

  • 8.
    Curic, Vladimir
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Salience-Based Parabolic Structuring Functions2013In: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer Berlin/Heidelberg, 2013, p. 183-194Conference paper (Refereed)
    Abstract [en]

    It has been shown that the use of the salience map based on the salience distance transform can be useful for the construction of spatially adaptive structuring elements. In this paper, we propose salience-based parabolic structuring functions that are defined for a fixed, predefined spatial support, and have low computational complexity. In addition, we discuss how to properly define adjunct morphological operators using the new spatially adaptive structuring functions. It is also possible to obtain flat adaptive structuring elements by thresholding the salience-based parabolic structuring functions.

  • 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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Borgefors, Gunilla
    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.
    Adaptive structuring elements based on salience distance transform2012In: In Proceedings of Swedish Society for Image Analysis, SSBA 2012, KTH, Stockholm, 2012Conference paper (Other academic)
    Abstract [en]

    Spatially adaptive structuring elements adjust their shape to the local structures in the image, and are often defined by a ball in a geodesic distance or gray-weighted distance metric space. This paper introduces salience adaptive structuring elements as spatially variant structuring elements that modify not only their shape, but also their size according to the salience of the edges in the image. Consequently they have good properties for filtering.

  • 10.
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Borgefors, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Salience adaptive structuring elements2012In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 6, no 7, p. 809-819Article in journal (Refereed)
    Abstract [en]

    Spatially adaptive structuring elements adjust their shape to the local structures in the image, and are often defined by a ball in a geodesic distance or gray-weighted distance metric space. This paper introduces salience adaptive structuring elements as spatially variant structuring elements that modify not only their shape, but also their size according to the salience of the edges in the image. Morphological operators with salience adaptive structuring elements shift edges with high salience to a less extent than those with low salience. Salience adaptive structuring elements are less flexible than morphological amoebas and their shape is less affected by noise in the image. Consequently, morphological operators using salience adaptive structuring elements have better properties.

  • 11.
    Fakhrzadeh, Azadeh
    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.
    Sporndly-Nees, Ellinor
    Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, Uppsala, Sweden..
    Ekstedt, Elisabeth
    Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, Uppsala, Sweden..
    Holm, Lena
    Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, Uppsala, Sweden..
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. U.
    New computerized staging method to analyze mink testicular tissue in environmental research2017In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 36, no 1, p. 156-164Article in journal (Refereed)
    Abstract [en]

    Histopathology of testicular tissue is considered to be the most sensitive tool to detect adverse effects on male reproduction. When assessing tissue damage, seminiferous epithelium needs to be classified into different stages to detect certain cell damages; but stage identification is a demanding task. The authors present a method to identify the 12 stages in mink testicular tissue. The staging system uses Gata-4 immunohistochemistry to visualize acrosome development and proved to be both intraobserver-reproducible and interobserver-reproducible with a substantial agreement of 83.6% (kappa=0.81) and 70.5% (kappa=0.67), respectively. To further advance and objectify this method, they present a computerized staging system that identifies these 12 stages. This program has an agreement of 52.8% (kappa 0.47) with the consensus staging by 2 investigators. The authors propose a pooling of the stages into 5 groups based on morphology, stage transition, and toxicologically important endpoints. The computerized program then reached a substantial agreement of 76.7% (kappa=0.69). The computerized staging tool uses local ternary patterns to describe the texture of the tubules and a support vector machine classifier to learn which textures correspond to which stages. The results have the potential to modernize the tedious staging process required in toxicological evaluation of testicular tissue, especially if combined with whole-slide imaging and automated tubular segmentation. Environ Toxicol Chem 2017;36:156-164.

  • 12.
    Fakhrzadeh, Azadeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Analyzing Tubular Tissue in Histopathological Thin Sections2012In: 2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA), IEEE conference proceedings, 2012, p. 1-6Conference paper (Refereed)
  • 13.
    Fakhrzadeh, Azadeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated measurement of epithelial height of testicular tissue2012In: Proceedings of Swedish Society for Image Analysis, SSBA 2012, Stockholm: KTH Royal Institute of Technology, 2012Conference paper (Other academic)
  • 14.
    Fakhrzadeh, Azadeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Epithelial Cell Layer Segmentation UsingGraph-cut and Its Application in TesticularTissue2013Conference paper (Refereed)
  • 15.
    Fakhrzadeh, Azadeh
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut2013In: Image Analysis and Processing – ICIAP 2013: Part II, 2013, p. 201-208Conference paper (Refereed)
    Abstract [en]

    Computerized image processing has provided us with valuable tools for analyzing histology images. However, histology images are complex, and the algorithm which is developed for a data set may not work for a new and unseen data set. The preparation procedure of the tissue before imaging can significantly affect the resulting image. Even for the same staining method, factors like delayed fixation may alter the image quality. In this paper we face the challenging problem of designing a method that works on data sets with strongly varying quality. In environmental research, due to the distance between the site where the wild animals are caught and the laboratory, there is always a delay in fixation. Here we suggest a segmentation method based on the structural information of epithelium cell layer in testicular tissue. The cell nuclei are detected using the fast radial symmetry filter. A graph is constructed on top of the epithelial cells. Graph-cut optimization method is used to cut the links between cells of different tubules. The algorithm is tested on five different groups of animals. Group one is fixed immediately, three groups were left at room temperature for 18, 30 and 42 hours respectively, before fixation. Group five was frozen after 6 hours in room temperature and thawed. The suggested algorithm gives promising results for the whole data set.

  • 16.
    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)
  • 17. Hall, Hardy C.
    et al.
    Fakhrzadeh, Azadeh
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Fischer, Urs
    Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images2016In: Frontiers in Plant Science, ISSN 1664-462X, E-ISSN 1664-462X, Vol. 7, article id 119Article in journal (Refereed)
    Abstract [en]

    While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to (1) segment radial plant organs into individual cells, (2) classify cells into cell type categories based upon Random Forest classification, (3) divide each cell into sub-regions, and (4) quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

  • 18.
    Joffre, Thomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
    Wernersson, Erik L. G.
    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.
    Miettinen, Arttu
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gamstedt, E. Kristofer
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics.
    Swelling of cellulose fibres in composite materials: Constraint effects of the surrounding matrix2013In: Composites Science And Technology, ISSN 0266-3538, E-ISSN 1879-1050, Vol. 74, p. 52-59Article in journal (Refereed)
  • 19.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Constrained and Dimensionality-Independent Path Openings2010In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 19, no 6, p. 1587-1595Article in journal (Refereed)
  • 20.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Path openings and their applications2010In: Proceedings SSBA 2010: Symposium on Image Analysis / [ed] C.L. Luengo Hendriks and M. Gavrilovic, Uppsala: Centre for Image Analysis , 2010, p. 79-82Conference paper (Other academic)
  • 21.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Revisiting priority queues for image analysis2010In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 43, no 9, p. 3003-3012Article in journal (Refereed)
    Abstract [en]

    Many algorithms in image analysis require a priority queue, a data structure that holds pointers to pixels in the image, and which allows efficiently finding the pixel in the queue with the highest priority. However, very few articles describing such image analysis algorithms specify which implementation of the priority queue was used. Many assessments of priority queues can be found in the literature, but mostly in the context of numerical simulation rather than image analysis. Furthermore, due to the ever-changing characteristics of computing hardware, performance evaluated empirically 10 years ago is no longer relevant. In this paper I revisit priority queues as used in image analysis routines, evaluate their performance in a very general setting, and come to a very different conclusion than other authors: implicit heaps are the most efficient priority queues. At the same time. I propose a simple modification of the hierarchical queue (or bucket queue) that is more efficient than the implicit heap for extremely large queues.

  • 22.
    Luengo Hendriks, Cris L.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Borgefors, GunillaUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.Strand, RobinUppsala 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.
    Mathematical Morphology and Its Applications to Signal and Image Processing: 11th International Symposium, ISMM 2013; Uppsala, Sweden, May 2013; Proceedings2013Conference proceedings (editor) (Refereed)
  • 23.
    Luengo Hendriks, Cris L.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Gavrilovic, MilanUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Proceedings SSBA 2010: Symposium on Image Analysis2010Conference proceedings (editor) (Other academic)
  • 24.
    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)
  • 25.
    Luengo Hendriks, Cris L.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Yu, Zi Quan
    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.
    Lecocq, Antoine
    Bakker, Teatske
    Locke, Barbara
    Dept of Ecology, Swedish University of Agricultural Sciences.
    Terenius, Olle
    Dept of Ecology, Swedish University of Agricultural Sciences.
    Identifying all individuals in a honeybee hive: progress towards mapping all social interactions2013Conference paper (Other academic)
  • 26.
    Luengo Hendriks, Cris
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Yu, Ziquan
    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.
    Lecocq, Antoine
    Bakker, Teatske
    Locke, Barbara
    Dept of Ecology, Swedish University of Agricultural Sciences.
    Terenius, Olle
    Dept of Ecology, Swedish University of Agricultural Sciences.
    Identifying all individuals in a honeybee hive: progress towards mapping all social interactions2012In: Visual observation and analysis of animal and insect behavior / [ed] R. Fisher, J. Hallam and B. Boom, 2012, p. 5-8Conference paper (Refereed)
  • 27.
    Malmberg, Filip
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    An efficient algorithm for exact evaluation of stochastic watersheds2014In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 47, p. 80-84Article in journal (Refereed)
    Abstract [en]

    The stochastic watershed is a method for unsupervised image segmentation proposed by Angulo and Jeulin (2007). The method first computes a probability density function (PDF), assigning to each piece of contour in the image the probability to appear as a segmentation boundary in seeded watershed segmentation with randomly selected seeds. Contours that appear with high probability are assumed to be more important. This PDF is then post-processed to obtain a final segmentation. The main computational hurdle with the stochastic watershed method is the calculation of the PDF. In the original publication by Angulo and Jeulin, the PDF was estimated by Monte Carlo simulation, i.e., repeatedly selecting random markers and performing seeded watershed segmentation. Meyer and Stawiaski (2010) showed that the PDF can be calculated exactly, without performing any Monte Carlo simulations, but do not provide any implementation details. In a naive implementation, the computational cost of their method is too high to make it useful in practice. Here, we extend the work of Meyer and Stawiaski by presenting an efficient (quasi-linear) algorithm for exact computation of the PDF. We demonstrate that in practice, the proposed method is faster than any previously reported method by more than two orders of magnitude. The algorithm is formulated for general undirected graphs, and thus trivially generalizes to images with any number of dimensions.

  • 28.
    Malmberg, Filip
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Selig, Bettina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Exact evaluation of stochastic watersheds: From trees to general graphs2014In: Discrete Geometry for Computer Imagery, Springer Berlin/Heidelberg, 2014, p. 309-319Conference paper (Refereed)
    Abstract [en]

    The stochastic watershed is a method for identifying salient contours in an image, with applications to image segmentation. The method computes a probability density function (PDF), assigning to each piece of contour in the image the probability to appear as a segmentation boundary in seeded watershed segmentation with randomly selected seedpoints. Contours that appear with high probability are assumed to be more important. This paper concerns an efficient method for computing the stochastic watershed PDF exactly, without performing any actual seeded watershed computations. A method for exact evaluation of stochastic watersheds was proposed by Meyer and Stawiaski (2010). Their method does not operate directly on the image, but on a compact tree representation where each edge in the tree corresponds to a watershed partition of the image elements. The output of the exact evaluation algorithm is thus a PDF defined over the edges of the tree. While the compact tree representation is useful in its own right, it is in many cases desirable to convert the results from this abstract representation back to the image, e. g, for further processing. Here, we present an efficient linear time algorithm for performing this conversion.

  • 29.
    Miettinen, Arttu
    et al.
    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, 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.
    Chinga-Carrasco, Gary
    Paper and Fibre Research Institute, Trondheim, Norway.
    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.
    A non-destructive X-ray microtomography approach for measuring fibre length in short-fibre composites2012In: Composites Science And Technology, ISSN 0266-3538, E-ISSN 1879-1050, Vol. 72, no 15, p. 1901-1908Article in journal (Refereed)
    Abstract [en]

    An improved method based on X-ray microtomography is developed for estimating fibre length distribution of short-fibre composite materials. In particular, a new method is proposed for correcting the biasing effects caused by the finite sample size as defined by the limited field of view of the tomographic devices. The method is first tested for computer generated fibre data and then applied in analyzing the fibre length distribution in three different types of wood fibre reinforced composite materials. The results were compared with those obtained by an independent method based on manual registration of fibres in images from a light microscope. The method can be applied in quality control and in verifying the effects of processing parameters on the fibre length and on the relevant mechanical properties of short fibre composite materials, e.g. stiffness, strength and fracture toughness. (C) 2012 Elsevier Ltd. All rights reserved.

  • 30. Miles, Cecelia M.
    et al.
    Lott, Susan E.
    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.
    Ludwig, Michael Z.
    Manu,
    Williams, Calvin L.
    Kreitman, Martin
    Artificial selection on egg size perturbs early pattern formation in Drosophila melanogaster2011In: Evolution, ISSN 0014-3820, E-ISSN 1558-5646, Vol. 65, no 1, p. 33-42Article in journal (Refereed)
  • 31.
    Rübel, Oliver
    et al.
    Lawrence Berkeley National Laboratory (California).
    Ahern, Sean
    Oak Ridge National Laboratory (Tennessee).
    Bethel, E. Wes
    Lawrence Berkeley National Laboratory (California).
    Biggin, Mark D.
    Lawrence Berkeley National Laboratory (California).
    Childs, Hank
    Lawrence Berkeley National Laboratory (California).
    Cormier-Michel, Estelle
    Tech-X Corporation (Colorado).
    DePace, Angela
    Harvard Medical School (Massachusetts).
    Eisen, Michael B.
    University of California, Berkeley.
    Fowlkes, Charless C.
    University of California, Irvine.
    Geddes, Cameron G.R.
    Lawrence Berkeley National Laboratory (California).
    Hagen, Hans
    University of Kaiserslautern (Germany).
    Hamann, Bernd
    Lawrence Berkeley National Laboratory (California).
    Huang, Min-Yu
    University of California, Davis.
    Keränen, Soile V.E.
    Lawrence Berkeley National Laboratory (California).
    Knowles, David W.
    Lawrence Berkeley National Laboratory (California).
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Malik, Jitendra
    University of California, Berkeley.
    Meredith, Jeremy
    Oak Ridge National Laboratory (Tennessee).
    Messmer, Peter
    Tech-X Corporation (Colorado).
    Prabhat, -
    Lawrence Berkeley National Laboratory (California).
    Ushizima, Daniela
    Lawrence Berkeley National Laboratory (California).
    Weber, Gunther H.
    Lawrence Berkeley National Laboratory (California).
    Wu, Kesheng
    Lawrence Berkeley National Laboratory (California).
    Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data2010In: Procedia Computer Science, ISSN 1877-0509, Vol. 1, no 1, p. 1751-1758Article in journal (Refereed)
  • 32.
    Selig, Bettina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Stochastic watershed – an analysis2012In: Proceedings of Swedish Society for Image Analysis, SSBA 2012, Stockholm: KTH Royal Institute of Technology, 2012Conference paper (Other academic)
  • 33.
    Selig, Bettina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bardage, Stig
    Daniel, Geoffrey
    Borgefors, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automatic measurement of compression wood cell attributes in fluorescence microscopy images2012In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 246, no 3, p. 298-308Article in journal (Refereed)
  • 34.
    Selig, Bettina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Fast evaluation of the robust stochastic watershed2015In: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer, 2015, p. 705-716Conference paper (Refereed)
    Abstract [en]

    The stochastic watershed is a segmentation algorithm that estimates the importance of each boundary by repeatedly segmenting the image using a watershed with randomly placed seeds. Recently, this algorithm was further developed in two directions: (1) The exact evaluation algorithm efficiently produces the result of the stochastic watershed with an infinite number of repetitions. This algorithm computes the probability for each boundary to be found by a watershed with random seeds, making the result deterministic and much faster. (2) The robust stochastic watershed improves the usefulness of the segmentation result by avoiding false edges in large regions of uniform intensity. This algorithm simply adds noise to the input image for each repetition of the watershed with random seeds. In this paper, we combine these two algorithms into a method that produces a segmentation result comparable to the robust stochastic watershed, with a considerably reduced computation time. We propose to run the exact evaluation algorithm three times, with uniform noise added to the input image, to produce three different estimates of probabilities for the edges. We combine these three estimates with the geometric mean. In a relatively simple segmentation problem, F-measures averaged over the results on 46 images were identical to those of the robust stochastic watershed, but the computation times were an order of magnitude shorter.

  • 35.
    Selig, Bettina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Vermeer, Koenraad A.
    Rieger, Bernd
    Hillenaar, Toine
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy2015In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, article id 13Article in journal (Refereed)
  • 36. Spörndly-Nees, Ellinor
    et al.
    Ekstedt, Elisabeth
    Magnusson, Ulf
    Fakhrzadeh, Azadeh
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Holm, Lena
    Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 5, article id e0125139Article in journal (Refereed)
  • 37.
    Tenow, Olle
    et al.
    Swedish University of Agricultural Sciences.
    Fagerström, Torbjörn
    Luengo, Cris
    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.
    Indication of an interspecies "spill-over" reaction in Common Swift Apus apus2009In: Ornis Svecica, ISSN 1102-6812, Vol. 19, no 4, p. 233-236Article in journal (Refereed)
  • 38. Van den Bulcke, Jan
    et al.
    Wernersson, Erik L. G.
    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.
    Dierick, Manuel
    Van Loo, Denis
    Masschaele, Bert
    Brabant, Loes
    Boone, Matthieu N.
    Van Hoorebeke, Luc
    Haneca, Kristof
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Van Acker, Joris
    3D tree-ring analysis using helical X-ray tomography2014In: Dendrochronologia, ISSN 1125-7865, E-ISSN 1612-0051, Vol. 32, no 1, p. 39-46Article in journal (Refereed)
    Abstract [en]

    The current state-of-the-art of tree-ring analysis and densitometry is still mainly limited to two dimensions and mostly requires proper treatment of the surface of the samples. In this paper we elaborate on the potential of helical X-ray computed tomography for 3D tree-ring analysis. Microdensitometrical profiles are obtained by processing of the reconstructed volumes. Correction of the structure direction, taking into account the angle of growth rings and grain, results in very accurate microdensity and precise ring width measurements. Both a manual as well as an automated methodology is proposed here, of which the MATLAB (c) code is available. Examples are given for pine (Pinus sylvestris L), oak (Quercus robur L) and teak (Tectona grandis L.). In all, the methodologies applied here on the 3D volumes are useful for growth related studies, enabling a fast and non-destructive analysis.

  • 39. Weber, Gunther H.
    et al.
    Rübel, Oliver
    Huang, Min-Yu
    DePace, Angela H.
    Fowlkes, Charless C.
    Keränen, Soile V .E
    Luengo Hendriks, Cris L.
    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.
    Hagen, Hans
    Knowles, David W.
    Malik, Jitendra
    Biggin, Mark D.
    Hamann, Bernd
    Visual exploration of three-dimensional gene expression using physical views and linked abstract views2009In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 6, no 2, p. 296-309Article in journal (Refereed)
    Abstract [en]

    During animal development, complex patterns of gene expression provide positional information within the embryo. To better understand the underlying gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed methods that support quantitative computational analysis of three-dimensional (3D) gene expression in early Drosophila embryos at cellular resolution. We introduce PointCloudXplore (PCX), an interactive visualization tool that supports visual exploration of relationships between different genes' expression using a combination of established visualization techniques. Two aspects of gene expression are of particular interest: 1) gene expression patterns defined by the spatial locations of cells expressing a gene and 2) relationships between the expression levels of multiple genes. PCX provides users with two corresponding classes of data views: 1) Physical Views based on the spatial relationships of cells in the embryo and 2) Abstract Views that discard spatial information and plot expression levels of multiple genes with respect to each other. Cell Selectors highlight data associated with subsets of embryo cells within a View. Using linking, these selected cells can be viewed in multiple representations. We describe PCX as a 3D gene expression visualization tool and provide examples of how it has been used by BDTNP biologists to generate new hypotheses.

  • 40.
    Wernersson, Erik
    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.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation of Wood Fibres in 3D CT Images Using Graph Cuts2009In: Image Analysis and Processing – ICIAP 2009, Berlin: Springer-Verlag , 2009, p. 92-102Conference paper (Refereed)
    Abstract [en]

    To completely segment all individual wood fibres in volume images of fibrous materials presents a challenging problem but is important in understanding the micro mechanical properties of composite materials. This paper presents a filter that identifies and closes pores in wood fibre walls, simplifying the shape of the fibres. After this filter, a novel segmentation method based on graph cuts identifies individual fibres. The methods are validated on a realistic synthetic fibre data set and then applied on μCT images of wood fibre composites.

  • 41.
    Wernersson, Erik L. G.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Boone, M. N.
    Ghent University.
    Van den Bulcke, J.
    Ghent University.
    Van Hoorebeke, L.
    Ghent University.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Understanding phase contrast artefacts in micro computed absorption tomography2014In: Proceedings SSBA 2014, Symposium on Image Analysis, 2014Conference paper (Other academic)
  • 42.
    Wernersson, Erik L. G.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Boone, Matthieu N.
    Van den Bulcke, Jan
    Van Hoorebeke, Luc
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Postprocessing method for reducing phase effects in reconstructed microcomputed-tomography data2013In: Optical Society of America. Journal A: Optics, Image Science, and Vision, ISSN 1084-7529, E-ISSN 1520-8532, Vol. 30, no 3, p. 455-461Article in journal (Refereed)
    Abstract [en]

    With increased resolution in x-ray computed tomography, refraction adds increasingly to the attenuation signal. Though potentially beneficial, the artifacts caused by refraction often need to be removed from the image. In this paper, we propose a postprocessing method, based on deconvolution, that is able to remove these artifacts after conventional reconstruction. This method poses two advantages over existing projection-based (preprocessing) phase-retrieval or phase-removal algorithms. First, evaluation of the parameters can be done very quickly, improving the overall speed of the method. Second, postprocessing methods can be applied when projection data is not available, which occurs in several commercial systems with closed software or when projection data has been deleted. It is shown that the proposed method performs comparably to state-of-the-art methods in terms of image quality.

  • 43.
    Wernersson, Erik L. G.
    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.
    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.
    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.
    Accurate estimation of Gaussian and mean curvature in volumetric images2011In: International Conference on 3D Imaging, Modeling, Processing, Visualization, and Transmission, 3DIMPVT 2011, IEEE Publications , 2011, p. 312-317Conference paper (Refereed)
  • 44.
    Wernersson, Erik L. G.
    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.
    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.
    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.
    Calculating curvature from orientation fields in volumetric images2011Conference paper (Other academic)
  • 45.
    Wernersson, Erik
    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.
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Generating synthetic μCT images of wood fibre materials2009In: Proc. 6th International Symposium on Image and Signal Processing and Analysis: ISPA 2009, Piscataway, NJ: IEEE , 2009, p. 365-370Conference paper (Refereed)
    Abstract [en]

    X-ray Computerized Tomography at micrometer resolution (μCT) is an important tool for understanding the properties of wood fibre materials such as paper, carton and wood fibre composites. While many image analysis methods have been developed for μCT images in wood science, the evaluation of these methods if often not thorough enough because of the lack of a dataset with ground truth. This paper describes the generation of synthetic μCT volumes of wood fibre materials. Fibres with a high degree of morphological variations are modeled and densely packed into a volume of the material. Using a simulation of the μCT image acquisition process, realistic synthetic images are obtained. This simulation uses noise characterized from a set of μCT images. The synthetic images have a known ground truth, and can therefore be used when evaluating image analysis methods.

1 - 45 of 45
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