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  • 151.
    Erikson, Mats
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Structure-preserving Segmentation of Individual Tree Crowns by Brownian Motion2003In: Proceedings of the 13th Scandinavian Conference on Image Analysis, 2003, p. 283-289Conference paper (Refereed)
  • 152.
    Erikson, Mats
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Two preprocessing techniques based on grey level and geometric thickness to improve segmentation results2006In: Pattern Recognition Letters, ISSN 0167-8655, Vol. 27, no 3, p. 160-166Article in journal (Refereed)
    Abstract [en]

    Two different techniques of performing preprocessing of an image to improve segmentation results are presented. The methods use the grey level thickness of the objects, in order to find the resulting image, by varying the size of a neighbourhood depending on the sum of the included grey levels. The first method, RW, uses the random walk of a particle, defined in the neighbourhood of the position of the particle. The resulting image holds the number of times the particle visits a pixel. Instead of randomization to find the number of visits, the second method, IP, scans the image iteratively and calculates the expected value of the same number. Three different kinds of real world applications are demonstrated to get better segmentation results with the preprocessing techniques included than without.

  • 153.
    Erikson, Mats
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Vestlund Karin,
    Finding tree-stems in laser range images of young mixed stands to perform selective cleaning2003In: Proceedings of the ScandLaser Scientific Workshop on Airborne Laser Scanning of Forest, 2003, p. 244-250Conference paper (Other scientific)
  • 154.
    Erlandsson, F
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Linnman, C
    Ekholm, S
    Bengtsson, E
    Zetterberg, A
    A detailed analysis of cyclin a accumulation at the G(1)/S border in normal and transformed cells2000In: EXPERIMENTAL CELL RESEARCH, ISSN 0014-4827, Vol. 259, no 1, p. 86-95Article in journal (Refereed)
    Abstract [en]

    The temporal relationship between cyclin A accumulation and the onset of DNA replication was analyzed in detail. Five untransformed and nine transformed asynchronously growing cell cultures were investigated using a triple immunofluorescence staining prot

  • 155.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Linnman-Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm, Susanna
    Bengtsson, Ewert
    Zetterberg, Anders
    A Detailed Analysis of Cyclin A Accumulation at the G1/S Border in Normal and Transformed Cells2000In: Experimental Cell Research, ISSN 0014-4827, Vol. 259, p. 86-95Article in journal (Refereed)
  • 156. Erlandsson, Fredrik
    et al.
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    University Administration. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Detection of large numbers of antigens using sequential immunofluorescence staining2001In: 7th European Society for Analytical Cellular Pathology Congress (ESACP 2001), Caen, France, 2001, p. 56-57Conference paper (Other scientific)
  • 157. Erlandsson, Fredrik
    et al.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm-Reed, Susanna
    Hellström, Ann-Cathrin
    Bengtsson, Ewert
    Zetterberg, Anders
    Abnormal expression pattern of cyclin E in tumor cells2003In: International Journal of Cancer, ISSN 0020-7136, Vol. 104, p. 369-375Article in journal (Refereed)
  • 158.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm-Reed, Susanna
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hellström, Ann-Cathrin
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Abnormal expression pattern of cyclin E in tumour cells2003In: Int J Cancer, ISSN 0020-7136, Vol. 104, p. 369-375Article in journal (Refereed)
  • 159.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby (nee Linnman), Carolina
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm, Susanna
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    A detailed analysis of cyclin A accumulation at the G1/S border in normal and transformed cells.2000In: Experimental Cell Research, ISSN 0014-4827/00, Vol. 256, p. 86-95Article in journal (Refereed)
    Abstract [en]

    Automatic cell segmentation has various applications in cytometry, and while the

    nucleus is often very distinct and easy to identify, the cytoplasm provides a lot

    more challenge. A new combination of image analysis algorithms for

    segmentation of cells imaged by fluorescence microscopy is presented. The

    algorithm consists of an image pre-processing step, a general segmentation

    and merging step followed by a segmentation quality measurement. The quality

    measurement consists of a statistical analysis of a number of shape descriptive

    features. Objects that have features that differ to that of correctly segmented

    single cells can be further processed by a splitting step. By statistical analysis

    we therefore get a feedback system for separation of clustered cells. After the

    segmentation is completed, the quality of the final segmentation is evaluated. By

    training the algorithm on a representative set of training images, the algorithm

    is made fully automatic for subsequent images created under similar conditions.

    Automatic cytoplasm segmentation was tested on CHO-cells stained with

    calcein. The fully automatic method showed between 89% and 97% correct

    segmentation as compared to manual segmentation.

  • 160.
    Ewert, Bengtsson
    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.
    Computer Assisted Analysis of Medical X-ray Images1996In: Symposium on X-rays in natural science and medicine, ROYAL SWEDISH ACAD SCIENCES , 1996, Vol. T61, p. 44-50Conference paper (Other academic)
  • 161.
    Flink, P
    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.
    Lindell, T
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Östlund, C
    Statistical analysis of hyperspectral data from two Swedish lakes2001In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 268, no 1-3, p. 155-169Article in journal (Refereed)
    Abstract [en]

    CASI data has been collected from two lakes in Sweden. In this paper. some statistical properties of CASI spectral data have been discussed. Principal component analysis is used for assessing the dimensionality of the data and the principal components wer

  • 162.
    Flink, Peter
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Aspects of the Chain of Processing and the Application of Multi- and Hyperspectral Data from Lakes1999Licentiate thesis, monograph (Other scientific)
  • 163. Forsberg, A-K
    et al.
    Pettersson, Lars W
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Human-Computer Interaction. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Människa-datorinteraktion.
    Lindén, E
    Sandberg, M
    Seipel, Stefan
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Human-Computer Interaction. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Människa-datorinteraktion.
    An augmented-reality approach to co-located visual exploration of indoor climate data in real rooms.2005In: Proceedings of the 10th International Conference on Indoor Air Quality and Climate: Indoor Air, 2005Conference paper (Refereed)
  • 164.
    Fouard, Celine
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Gedda, Magnus
    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.
    An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces2006In: Discrete Geometry for Computer Imagery: 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006, Proceedings, 2006, p. 259-270Conference paper (Refereed)
    Abstract [en]

    In this paper, we compare two different definitions of distance transform for gray level images: the Gray Weighted Distance Transform (GWDT), and the Weighted Distance Transform On Curved Space (WDTOCS). We show through theoretical and experimental comparisons the differences, the strengths and the weaknesses of these two distances.

  • 165. Fouard, Céline
    et al.
    Strand, Robin
    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.
    Weighted Distance Expression in Modules2006In: Proceedings SSBA'06 Symposium on Image Analysis, 2006Conference paper (Other academic)
    Abstract [en]

    This paper presents the different properties of weighted distance and generalizes them to a global framework: modules. This allows to use weighted distance on unusal grids.

  • 166. Fouard, Céline
    et al.
    Strand, Robin
    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.
    Weighted Distance Transforms Generalized To Modules and Their Computation on Point Lattices.2006Report (Other (popular science, discussion, etc.))
  • 167.
    Fouard, Céline
    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.
    Strand, Robin
    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.
    Borgefors, Gunilla
    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.
    Weighted distance transforms generalized to modules and their computation on point lattices2007In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 40, no 9, p. 2453-2474Article in journal (Refereed)
    Abstract [en]

    This paper presents the generalization of weighted distances to modules and their computation through the chamfer algorithm on general point lattices. The first part is dedicated to formalization of definitions and properties (distance, metric, norm) of weighted distances on modules. It resumes tools found in literature to express the weighted distance of any point of a module and to compute optimal weights in the general case to get rotation invariant distances. The second part of this paper proves that, for any point lattice, the sequential two-scan chamfer algorithm produces correct distance maps. Finally, the definitions and computation of weighted distances are applied to the face-centered cubic (FCC) and body-centered cubic (BCC) grids.

  • 168.
    Fransson, J.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Walter, F.
    Radar views the forest in a new light2000In: Fakta Skog, SLU, no 8, p. 1-4Article in journal (Other scientific)
  • 169.
    Fransson, J.E.S.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Gustavsson, A.
    Ulander, L.M.H.
    Walter, F.
    Mapping of forest stand parameters using VHF SAR data2000Conference paper (Refereed)
    Abstract [en]

    Forest stand parameters at high spatial resolution are needed for long term and

  • 170.
    Fransson, J.E.S.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Gustavsson, A.
    Ulander, L.M.H.
    Walter, F.
    Towards an operational use of VHF SAR data for forest mapping and forest management2000Conference paper (Refereed)
    Abstract [en]

    Forest stand parameters at high spatial resolution are needed for long and short

  • 171.
    Fransson, J.E.S.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Walter, F.
    Ulander, L.M.H.
    Estimation of forest parameters using CARABAS-II VHF SAR data2000In: IEEE Trans. on Geoscience and Remote Sensing, Vol. 38, no 2, p. 720-727Article in journal (Other scientific)
    Abstract [en]

    The use of airborne CARABAS-II VHF (20-90 MHz) SAR data for retrieval

  • 172.
    Frimmel, Hans
    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.
    Biopsy Needle Optimisation1997In: Proc. 10th Scandinavian Conference on Image Analysis, Pattern Recognition Society of Finland , 1997, p. 381-387Conference paper (Refereed)
  • 173.
    Frimmel, Hans
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Positioning Biopsy Needles in the Prostate Gland Using 3D Computer Modelling1999Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In the world of medicine, image diagnostics have, until recently, been based merely on two dimensional information sources. The understanding of three dimensional structures has been limited to creating mental images in the mind of the physician, to wax models and to autopsy. In the last few years, computers have made it possible to model and reconstruct real three dimensional objects and thus give the physician a new tool not only to describe localisation and distribution patterns of diseases, above all cancer, but also as an aid in the understanding of the human body. This thesis contributes in the development of such tools, based on a specific application.

    Prostate cancer is for men the most common form of cancer. Improvement in diagnostics for this form of cancer would facilitate planning of treatment and hence save, and preserve the quality of, life. One way to diagnose and quantify prostate cancer is to assess its presence and malignancy grade in cylindrical tissue samples taken with a needle biopsy device. Today, two to six such samples are generally taken, with poorly standardised rules for the positioning of the needle, thus interindividual variation exists.

    In this thesis, 3D models to analyse the problem with the positioning of biopsy needles have been developed. By using information from physical prostates removed from patients by surgery, a 3D cancer probability distribution has been built. Using this information, a standardised biopsy needle protocol has been created that is efficient, stable and easy to use. In this process new methods for morphing images, registrating slices and optimising positions for use with computer modelling have been developed.

    Many physicians were involved in the study. Thus, an important part of the work has been to make every part of the work understandable for people without special computer programming knowledge. Also, efforts have been made to make it possible to easily examine every piece of information created in order to verify the correctness of the methods used.

  • 174.
    Frimmel, Hans
    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.
    Egevad, Lars
    Bengtsson, Ewert
    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.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Modeling prostate cancer distributions1999In: Urology, ISSN 0090-4295, E-ISSN 1527-9995, Vol. 54, p. 1028-1034Article in journal (Refereed)
  • 175.
    Frimmel, Hans
    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.
    Egevad, Lars
    Busch, Christer
    Bengtsson, Ewert
    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.
    Automatic registration and error detection of multiple slices using landmarks2001In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 23, p. 159-165Article in journal (Refereed)
  • 176.
    Gamstedt, E. Kristofer
    et al.
    Department of Fibre and Polymer Technology, Royal Institute of Technology (KTH), Stockholm, Sweden.
    Almgren, Karin M.
    STFI-Packforsk AB, Division of Packaging and Logistics, Stockholm, Sweden.
    Selig, Bettina
    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.
    Bjurhager, Ingela
    Department of Fibre and Polymer Technology, Royal Institute of Technology (KTH), Stockholm, Sweden.
    Svensson, Stina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Nygård, Per
    PFI AS – Paper and Fibre Research Institute, Trondheim, Norway.
    X-ray Microtomography of Wood-Based Materials: Pulp-Fibre Reinforced Thermoplastics and Polythylen Glycol-Impregnated Oak2007Report (Other (popular science, discussion, etc.))
  • 177.
    Gavrilovic, Milan
    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.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Quantification of colocalization and cross-talk based on spectral angles2009In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 234, no 3, p. 311-324Article in journal (Refereed)
    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.

  • 178.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for cross-talk suppression in fluorescence microscopy2008In: Medicinteknikdagarna 2008, 2008, p. 64-64Conference paper (Other academic)
    Abstract [en]

     

     

     

  • 179.
    Gedda, M.
    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.
    Öfverstedt, L.-G.
    Okinawa Institute of Science and Technology, Okinawa, Japan.
    Skoglund, U.
    Okinawa Institute of Science and Technology, Okinawa, Japan.
    Svensson, S.
    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.
    Image Processing System for Localising Macromolecules in Cryo-Electron Tomography2010In: Machine Graphics & Vision, ISSN 1230-0535, Vol. 19, no 2, p. 159-184Article in journal (Refereed)
    Abstract [en]

    A major challenge in today's molecular biology research is to understand the interaction between proteins at the molecular level. Cryo-electron tomography (ET) has come to play an important role in facilitating objective qualitative experiments on protein structures and their interaction. Various protein conformation structures can be qualitatively analysed as complete galleries of proteins are captured by ET. To facilitate fast and objective macromolecular structure analysis procedures, image processing has become a crucial tool. This paper presents an image processing system for localising individual proteins from in vitro samples imaged by ET. We have evaluated the system using simulated data as well as experimental data.

  • 180.
    Gedda, Magnus
    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.
    Automatisk spårning av dendriter i konfokalmikroskopibilder av nervceller2008In: Medicinteknikdagarna 2008, 2008, p. 61-61Conference paper (Other academic)
  • 181.
    Gedda, Magnus
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements2005In: Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 2005, Proceedings, Part II, 2005, p. 809-Conference paper (Refereed)
    Abstract [en]

    This paper evaluates whether shape features can be used for clustering objects in Sidec (tm), Electron Tomography (SET) reconstructions. SET reconstructions contain a large number of objects, and only a few of them are of interest. It is desired to limit the analysis to contain as few uninteresting objects as possible. Unsupervised hierarchical clustering is used to group objects into classes. Experiments are done on one synthetic data set and two data sets from a SET reconstruction of a human growth hormone (1hwg) in solution. The experiments indicate that clustering of objects in SET reconstructions based on shape features is useful for finding structural classes.

  • 182.
    Gedda, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques: With Focus on Images from Cryo-Electron Tomography2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    With the emergence of new imaging techniques, researchers are always eager to push the boundaries by examining objects either smaller or further away than what was previously possible. The development of image analysis techniques has greatly helped to introduce objectivity and coherence in measurements and decision making. It has become an essential tool for facilitating both large-scale quantitative studies and qualitative research. In this Thesis, methods were developed for analysis of low-resolution (in respect to the size of the imaged objects) three-dimensional (3D) images with low signal-to-noise ratios (SNR) applied to images from cryo-electron tomography (cryo-ET) and fluorescence microscopy (FM). The main focus is on methods of low complexity, that take into account both grey-level and shape information, to facilitate large-scale studies. Methods were developed to localise and represent complex macromolecules in images from cryo-ET. The methods were applied to Immunoglobulin G (IgG) antibodies and MET proteins. The low resolution and low SNR required that grey-level information was utilised to create fuzzy representations of the macromolecules. To extract structural properties, a method was developed to use grey-level-based distance measures to facilitate decomposition of the fuzzy representations into sub-domains. The structural properties of the MET protein were analysed by developing a analytical curve representation of its stalk. To facilitate large-scale analysis of structural properties of nerve cells, a method for tracing neurites in FM images using local path-finding was developed. Both theoretical and implementational details of computationally heavy approaches were examined to keep the time complexity low in the developed methods. Grey-weighted distance definitions and various aspects of their implementations were examined in detail to form guidelines on which definition to use in which setting and which implementation is the fastest. Heuristics were developed to speed up computations when calculating grey-weighted distances between two points. The methods were evaluated on both real and synthetic data and the results show that the methods provide a step towards facilitating large-scale studies of images from both cryo-ET and FM.

    List of papers
    1. Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
    Open this publication in new window or tab >>Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
    2005 (English)In: Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 2005, Proceedings, Part II, 2005, p. 809-Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper evaluates whether shape features can be used for clustering objects in Sidec (tm), Electron Tomography (SET) reconstructions. SET reconstructions contain a large number of objects, and only a few of them are of interest. It is desired to limit the analysis to contain as few uninteresting objects as possible. Unsupervised hierarchical clustering is used to group objects into classes. Experiments are done on one synthetic data set and two data sets from a SET reconstruction of a human growth hormone (1hwg) in solution. The experiments indicate that clustering of objects in SET reconstructions based on shape features is useful for finding structural classes.

    Keywords
    clustering, shape, electron tomography, protein
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-74174 (URN)doi:10.1007/11552499_43 (DOI)3-540-28833-3 (ISBN)
    Available from: 2005-10-05 Created: 2005-10-05 Last updated: 2018-01-14
    2. Separation of blob-like structures using fuzzy distance based hierarchical clustering
    Open this publication in new window or tab >>Separation of blob-like structures using fuzzy distance based hierarchical clustering
    2006 (English)In: Symposium on Image Analysis: SSBA 2006, Umeå, Sweden, March 16-17, 2006, Proceedings, 2006Conference paper, Published paper (Other academic)
    Abstract [en]

    We present a method for separation of clustered biomedical blob-like structures in 2D and 3D images. The local maxima found on the fuzzy distance transform of the image are grouped by fuzzy distance based hierarchical clustering and used as seeds in a seeded watershed segmentation to delineate each individual blob. The method shows good initial results and is illustrated on two types of images: bright field microscopy (2D) images of in vitro stem cells and cryo electron tomography (3D) images of the antibody immunoglobulin G.

    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-21703 (URN)
    Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2018-01-12Bibliographically approved
    3. Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm
    Open this publication in new window or tab >>Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm
    2006 (English)In: Combinatorial Image Analysis: 11th International Workshop, IWCIA 2006, Berlin, Germany, June 19-21, 2006, Proceedings, 2006, p. 101-115Conference paper, Published paper (Refereed)
    Abstract [en]

    We present a method for calculating fuzzy distances between pairs of points in an image using the A* algorithm and, furthermore, apply this method for fuzzy distance based hierarchical clustering. The method is general and can be of use in numerous applications. In our case we intend to use the clustering in an algorithm for delineation of objects corresponding to parts of proteins in 3D images. The image is defined as a fuzzy object and represented as a graph, enabling a path finding approach for distance calculations. The fuzzy distance between two adjacent points is used as edge weight and a heuristic is defined for fuzzy sets. A* is applied to the calculation of fuzzy distance between pair of points and hierarchical clustering is used to group the points. The normalised Hubert's statistic is used as validity index to determine the number of clusters. The method is tested on three 2D images; two synthetic images and one fuzzy distance transformed microscopy image of stem cells. All experiments show promising initial results.

    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 4040
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:uu:diva-21701 (URN)10.1007/11774938_9 (DOI)3-540-35153-1 (ISBN)
    Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2010-03-25Bibliographically approved
    4. An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces
    Open this publication in new window or tab >>An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces
    2006 (English)In: Discrete Geometry for Computer Imagery: 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006, Proceedings, 2006, p. 259-270Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper, we compare two different definitions of distance transform for gray level images: the Gray Weighted Distance Transform (GWDT), and the Weighted Distance Transform On Curved Space (WDTOCS). We show through theoretical and experimental comparisons the differences, the strengths and the weaknesses of these two distances.

    Series
    Lecture notes in computer science, ISSN 0302-9743 ; 4245
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-21702 (URN)10.1007/11907350_22 (DOI)978-3-540-47651-1 (ISBN)
    Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2018-01-12Bibliographically approved
    5. Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
    Open this publication in new window or tab >>Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
    2007 (English)In: 12th International Conference on Computer Analysis of Images and Patterns, 2007, p. 173-180Conference paper, Published paper (Refereed)
    Abstract [en]

    The MET protein controls growth, invasion, and metastasis in cancer cells and is thereby of interest to study, for example from a structural point of view. For individual particle imaging by Cryo-Electron Tomography of the MET protein, or other proteins, dedicated image analysis methods are required to extract information in a robust way as the images have low contrast and resolution (with respect to the size of the imaged structure). We present a method to identify the two parts of the MET protein, Beta-propeller and stalk, using a fuzzy framework. Furthermore, we describe how a representation of the MET stalk, denoted stalk curve, can be identified based on the use of non-uniform B-splines. The stalk curve is used to extract relevant geometrical information about the stalk, e.g., to facilitate curvature and length measurements.

    Keywords
    Cryo-ET, fuzzy distance, geometrical feature extraction
    Identifiers
    urn:nbn:se:uu:diva-11902 (URN)doi:10.1007/978-3-540-74272-2_22 (DOI)978-3-540-74271-5 (ISBN)
    Available from: 2007-11-05 Created: 2007-11-05 Last updated: 2010-03-25
    6. Three-Dimensional Tracing of Neurites in Fluorescence Microscopy Images Using Local Path-Finding
    Open this publication in new window or tab >>Three-Dimensional Tracing of Neurites in Fluorescence Microscopy Images Using Local Path-Finding
    2010 (English)In: 2010 IEEE International Conference On Acoustics, Speech And Signal Processing, 2010. ICASSP 2010, 2010, p. 646-649Conference paper, Published paper (Refereed)
    Abstract [en]

    Neurite tracing in 3D neuron images is important when it comes to analysing the growth and functionality of nerve cells. The methods used today are either of high complexity, limiting throughput, or semi-automatic, i.e., requiring user interaction. This makes them unsuitable for analysis where high throughput is needed. In this work we propose a method designed for low complexity and void of user interaction by using local path-finding. The method is illustrated on both phantom and real data, and compared with a widely used commercial software package with promising results.

    Series
    International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
    Keywords
    Tracing, neurite, path-finding, 3D
    National Category
    Computer and Information Sciences
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121571 (URN)000287096000158 ()978-1-4244-4296-6 (ISBN)
    Conference
    2010 IEEE International Conference on Acoustics, Speech, and Signal Processing Dallas, TX, MAR 14-19, 2010
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2018-01-12Bibliographically approved
    7. Heuristics for grey-weighted distance computations
    Open this publication in new window or tab >>Heuristics for grey-weighted distance computations
    2010 (English)In: Symposium on Image Analysis, Uppsala, March 11-12. Proceedings SSBA 2010., 2010Conference paper, Published paper (Other academic)
    Abstract [en]

    With new imaging techniques and computing power come increasing demands on the computation cost of image analysis algorithms. Grey-weighted distance transforms are traditionally computed using graph-based region-growing algorithms. These algorithms expand nodes in all directions by evolving an isotropic wave front. When calculating point-to-point distances, the isotropic propagation can be unnecessarily costly since it expands many nodes in directions away from the goal node. By introducing a heuristic to guide the search, the number of excessive nodes can be decreased. Here we introduce heuristics for computing Distance on Curved Spaces and Weighted Distance on Curved Spaces. We also examine the impact of these heuristics together with a heuristic previously proposed for fuzzy distance computations. The results show that the number of nodes expanded in point-to-point grey-weighted distances can be decreased by up to ~79%.

    National Category
    Computer and Information Sciences
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121572 (URN)
    Conference
    SSBA 2010
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2018-01-12Bibliographically approved
    8. Image Processing System for Localising Macromolecules in Cryo-Electron Tomography
    Open this publication in new window or tab >>Image Processing System for Localising Macromolecules in Cryo-Electron Tomography
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    A major challenge in today's molecular biology research is to understand the interaction between proteins at the molecular level. Cryo-electron tomography (ET) has come to play an important role in facilitating objective qualitative experiments on protein structures and their interaction. Various protein conformation structures can be qualitatively analysed as complete galleries of proteins are captured by ET. To facilitate fast and objective macromolecular structure analysis procedures, image processing has become a crucial tool. This paper presents an image processing system for localising individual proteins from in vitro samples imaged by ET. We have evaluated the system using simulated data as well as experimental data.

    Keywords
    Fuzzy set, watershed segmentation, distance transform, proteins
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121573 (URN)
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2010-03-25
    9. Algorithms for Grey-Weighted Distance Computations
    Open this publication in new window or tab >>Algorithms for Grey-Weighted Distance Computations
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    With the increasing size of datasets and demand for real time response for interactive applications, improving runtime for algorithms with excessive computational requirements has become increasingly important. Many different algorithms combining efficient priority queues with various helper structures have been proposed for computing grey-weighted distance transforms. Here we compare the performance of popular competitive algorithms in different scenarios to form practical guidelines easy to adopt. The label-setting category of algorithms is shown to be the best choice for all scenarios. The hierarchical heap with a pointer array to keep track of nodes on the heap is shown to be the best choice as priority queue. However, if memory is a critical issue, then the best choice is the Dial priority queue for integer valued costs and the Untidy priority queue for real valued costs.

    Keywords
    Grey-weighted distance, Geodesic time, Geodesic distance, Fuzzy distance, Algorithms, Region growing
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121578 (URN)
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2010-03-25
  • 183.
    Gedda, Magnus
    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.
    Heuristics for grey-weighted distance computations2010In: Symposium on Image Analysis, Uppsala, March 11-12. Proceedings SSBA 2010., 2010Conference paper (Other academic)
    Abstract [en]

    With new imaging techniques and computing power come increasing demands on the computation cost of image analysis algorithms. Grey-weighted distance transforms are traditionally computed using graph-based region-growing algorithms. These algorithms expand nodes in all directions by evolving an isotropic wave front. When calculating point-to-point distances, the isotropic propagation can be unnecessarily costly since it expands many nodes in directions away from the goal node. By introducing a heuristic to guide the search, the number of excessive nodes can be decreased. Here we introduce heuristics for computing Distance on Curved Spaces and Weighted Distance on Curved Spaces. We also examine the impact of these heuristics together with a heuristic previously proposed for fuzzy distance computations. The results show that the number of nodes expanded in point-to-point grey-weighted distances can be decreased by up to ~79%.

  • 184.
    Gedda, Magnus
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Svensson, Stina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines2007In: 12th International Conference on Computer Analysis of Images and Patterns, 2007, p. 173-180Conference paper (Refereed)
    Abstract [en]

    The MET protein controls growth, invasion, and metastasis in cancer cells and is thereby of interest to study, for example from a structural point of view. For individual particle imaging by Cryo-Electron Tomography of the MET protein, or other proteins, dedicated image analysis methods are required to extract information in a robust way as the images have low contrast and resolution (with respect to the size of the imaged structure). We present a method to identify the two parts of the MET protein, Beta-propeller and stalk, using a fuzzy framework. Furthermore, we describe how a representation of the MET stalk, denoted stalk curve, can be identified based on the use of non-uniform B-splines. The stalk curve is used to extract relevant geometrical information about the stalk, e.g., to facilitate curvature and length measurements.

  • 185.
    Gedda, Magnus
    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.
    Svensson, Stina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm2006In: Combinatorial Image Analysis: 11th International Workshop, IWCIA 2006, Berlin, Germany, June 19-21, 2006, Proceedings, 2006, p. 101-115Conference paper (Refereed)
    Abstract [en]

    We present a method for calculating fuzzy distances between pairs of points in an image using the A* algorithm and, furthermore, apply this method for fuzzy distance based hierarchical clustering. The method is general and can be of use in numerous applications. In our case we intend to use the clustering in an algorithm for delineation of objects corresponding to parts of proteins in 3D images. The image is defined as a fuzzy object and represented as a graph, enabling a path finding approach for distance calculations. The fuzzy distance between two adjacent points is used as edge weight and a heuristic is defined for fuzzy sets. A* is applied to the calculation of fuzzy distance between pair of points and hierarchical clustering is used to group the points. The normalised Hubert's statistic is used as validity index to determine the number of clusters. The method is tested on three 2D images; two synthetic images and one fuzzy distance transformed microscopy image of stem cells. All experiments show promising initial results.

  • 186.
    Gedda, Magnus
    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.
    Svensson, Stina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Separation of blob-like structures using fuzzy distance based hierarchical clustering2006In: Symposium on Image Analysis: SSBA 2006, Umeå, Sweden, March 16-17, 2006, Proceedings, 2006Conference paper (Other academic)
    Abstract [en]

    We present a method for separation of clustered biomedical blob-like structures in 2D and 3D images. The local maxima found on the fuzzy distance transform of the image are grouped by fuzzy distance based hierarchical clustering and used as seeds in a seeded watershed segmentation to delineate each individual blob. The method shows good initial results and is illustrated on two types of images: bright field microscopy (2D) images of in vitro stem cells and cryo electron tomography (3D) images of the antibody immunoglobulin G.

  • 187.
    Göransson, Jenny
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Isaksson, Magnus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Howell, Mathias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Jarvius, Jonas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Nilsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    A single molecule array for digital targeted molecular analyses2009In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 37, no 1, p. e7-Article in journal (Refereed)
    Abstract [en]

    We present a new random array format together with a decoding scheme for targeted multiplex digital molecular analyses. DNA samples are analyzed using multiplex sets of padlock or selector probes that create circular DNA molecules upon target recognition. The circularized DNA molecules are amplified through rolling-circle amplification (RCA) to generate amplified single molecules (ASMs). A random array is generated by immobilizing all ASMs on a microscopy glass slide. The ASMs are identified and counted through serial hybridizations of small sets of tag probes, according to a combinatorial decoding scheme. We show that random array format permits at least 10 iterations of hybridization, imaging and dehybridization, a process required for the combinatorial decoding scheme. We further investigated the quantitative dynamic range and precision of the random array format. Finally, as a demonstration, the decoding scheme was applied for multiplex quantitative analysis of genomic loci in samples having verified copy-number variations. Of 31 analyzed loci, all but one were correctly identified and responded according to the known copy-number variations. The decoding strategy is generic in that the target can be any biomolecule which has been encoded into a DNA circle via a molecular probing reaction.

  • 188.
    Haindl, W
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lundqvist, R
    Som, S
    Mitchell, G
    Frawley, J
    A statistical analysis of cerebral cortical perfusion changes following carotid endarterectomy2001Other (Other scientific)
  • 189.
    Hamid Muhammed, H
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Characterizing and Estimating Fungal Disease Severity in Wheat2004In: Swedish Society for Automated Image Analysis Symposium - SSBA 2004, Ångströmlaboratoriet, Uppsala University, 2004, p. 194-198Conference paper (Other scientific)
  • 190.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Determination of Water Quality of Lake Erken, Sweden, by using Feature-Vector Based Analysis2001In: Irish Machine Vision and Image Processing Conference (IMVIP 2001), Maynooth, Ireland, 2001, p. 261-Conference paper (Other scientific)
  • 191.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis2001In: Irish Machine Vision and Image Processing Conference (IMVIP 2001), Maynooth, Ireland, 2001, p. 219-226Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel technique, which can be called Feature-Vector Based

  • 192.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks2003Conference paper (Refereed)
    Abstract [en]

    A new class of neuro-fuzzy systems, based on so-called Weighted Neural Networks (WNN), is introduced and used for unsupervised fuzzy clustering and image segmentation. Incremental and fixed (or grid-partitioned) Weighted Neural Networks are presented and

  • 193.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Hyperspectral Image Segmentation Using a New Class of Neuro-Fuzzy Systems Based on Weighted Incremental Neural Networks2002In: 31st Applied Imagery Pattern Recognition Worshop (AIPR 2002), Washington DC, USA, 2002Conference paper (Other scientific)
    Abstract [en]

    Segmenting hyperspectral images is an important task for simplifying the

  • 194.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Image Segmentation Using New Neuro-Fuzzy Systems2002In: Swedish Society for Automated Image Analysis Symposium - Proceedings of SSAB 2002, 2002, p. 83-87Conference paper (Other scientific)
  • 195.
    Hamid muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Using Hyperspectral Reflectance Data for Discrimination Between Healthyand Diseased Plants, and Determination of Damage-Level in Diseased Plants2002In: 31st Applied Imagery Pattern Recognition Worshop (AIPR 2002), Washington DC, USA., 2002Conference paper (Other scientific)
    Abstract [en]

    It has been proven, through many research works, that hyperspectral

  • 196.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering2002In: International Journal of Neural Systems (IJNS), Vol. 12, no 6, p. 425-434Article in journal (Refereed)
    Abstract [en]

    A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Networks (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces

  • 197.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    What is Feature­Vector Based Analysis?2001In: Swedish Society for Automated Image Analysis Symposium - SSAB 2001,ITN, Campus Norrköping, LinköpingUniversity, 2001, p. 131-134Conference paper (Other scientific)
  • 198.
    Hamid Muhammed, H.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ammenberg, P.
    Bengtsson, E.
    Using Feature-Vector Based Analysis, based on Principal Component Analysis andIndependent Component Analysis, for Analysing Hyperspectral Images2001In: 11th International Conference for Image Analysis and Processing (ICIAP2001), Palermo, Italy, 2001, p. 309-315Conference paper (Refereed)
    Abstract [en]

    A pixel in a hyperspectral image can be considered as a mixture of the reflectance

  • 199.
    Hamid Muhammed, H.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsolle, A.
    Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat2003In: Biosystems Engineering, Vol. 86, no 2, p. 125-134Article in journal (Refereed)
    Abstract [en]

    The impact of plant pathological stress on crop reflectance can be measured both in broad-band vegetation indices and in narrow or local characteristics of the reflectance spectra. This work is concerned with using the whole spectra in the objective exami

  • 200.
    Hamid Muhammed, Hamed
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis2001In: Proceedings of Irish Machine Vision and Image Processing Conference, p. 219-226Article in journal (Refereed)
1234567 151 - 200 of 679
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