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  • 401.
    Nyström, I.
    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.
    Udupa, J. K.
    Grevera, G. J.
    Hirsch, B. E.
    Area of and volume enclosed by digital and triangulated surfaces2002In: Medical Imaging 2002: Visualization, Image-Guided Procedures, and Display, 2002, p. 669-680Conference paper (Refereed)
    Abstract [en]

    We demonstrate that the volume enclosed by triangulated surfaces can be computed efficiently in the same elegant way the volume enclosed by digital surfaces is computed by digital surface integration.

  • 402.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    On Quantitative Shape Analysis of Digital Volume Images1997Doctoral thesis, comprehensive summary (Other academic)
  • 403.
    Nyström, Ingela
    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.
    Skeletonization applied to magnetic resonance angiography images1998In: SPIE Medical Imaging 1998, SPIE Publications No 3338 , 1998, p. 693-701Conference paper (Refereed)
    Abstract [en]

    When interpreting and analysing magnetic resonance angiography images, the 3D overall tree structure and the thickness of the blood vessels are of interest. This shape information may be easier to obtain from the skeleton of the blood vessels. Skeletoniza

  • 404.
    Nyström, Ingela
    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.
    Borgefors, Gunilla
    Synthesising Objects and Scenes using the Reverse Distance Transformation in 2D and 3D1995In: 8th International Conference on Image Analysis and Processing, Springer Verlag , 1995, p. 441-446Conference paper (Refereed)
  • 405.
    Nyström, Ingela
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sanniti di Baja, Gabriella
    Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    2D grey-level convex hull computation: a discrete 3D approach2003Conference paper (Refereed)
    Abstract [en]

    We compute discrete convex hulls in 2D grey-level images, where we interpret grey-level values as heights in 3D landscapes. For these 3D objects, using a 3D binary method, we compute approximations of their convex hulls. Differently from other grey-level

  • 406.
    Nyström, Ingela
    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.
    Holmgren, SverkerUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    UPPMAX Progress Report2005Collection (editor) (Other (popular science, discussion, etc.))
  • 407.
    Nyström, Ingela
    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, Centre for Image Analysis.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Vidholm, Erik
    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.
    Segmentation and Visualization of 3D Medical Images through Haptic Rendering2009In: Proceedings of the 10th International Conference on Pattern Recognition and Information Processing (PRIP 2009), Minsk, Belarus: Publishing Center of BSU , 2009, p. 43-48Conference paper (Refereed)
    Abstract [en]

    High-dimensional and high-resolution image data is increasingly produced by modern medical imaging equipment. As a consequence, the need for efficient interactive tools for segmentation and visualization of these medical images is also increasing. Existing software include state-of-the-art algorithms, but in most cases the interaction part is limited to 2D mouse/keyboard, despite the tasks being highly 3D oriented. This project involves interactive medical image visualization and segmentation, where true 3D interaction is obtained with stereo graphics and haptic feedback. Well-known image segmentation algorithms, e.g., fast marching, fuzzy connectedness, deformable models, and live-wire, have been implemented in a framework allowing the user to interact with the algorithms and the volumetric data in an efficient manner. The data is visualized via multi-planar reformatting, surface rendering, and hardware-accelerated volume rendering. We present a case study where liver segmentation is performed in CT images with high accuracy and precision.

  • 408.
    Nyström, Ingela
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sanniti di Baja, Gabriella
    Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Skeletonization in 3D Discrete Binary Images2005In: Handbook of Pattern Recognition and Computer Vision, 3rd edition, World Scientific, Singapore , 2005, p. 137-156Chapter in book (Other (popular scientific, debate etc.))
    Abstract [en]

    Skeletonization is a way to reduce dimensionality of digital objects. Here, we present in detail an algorithm that computes the curve skeleton of a solid object, i.e., an object without cavities, in a 3D binary image. The algorithm consists of three main steps. During the first step, the surface skeleton is detected, by directly marking in the distance transform of the object the voxels that should be assigned to the surface skeleton. The curve skeleton is then computed by iteratively thinning the surface skeleton, during the second step. Finally, the third step is performed to reduce the curve skeleton to unit width and to prune, in a controlled manner, some of its peripheral branches.

  • 409.
    Nyström, Ingela
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sanniti di Baja, GabriellaTeknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.Svensson, StinaUppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Discrete Applied Mathematics, 147(2-3):147-361: Special issue on Discrete Geometry for Computer Imagery2005Collection (editor) (Other scientific)
  • 410.
    Nyström, Ingela
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sanniti di Baja, GabriellaSvensson, StinaUppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Image and Vision Computing 23(2):87-269: Special issue on Discrete Geometry for Computer Imagery2005Collection (editor) (Other scientific)
  • 411.
    Ohlsson, Henrik
    et al.
    Department of Electrical Engineering, Linköpings universitet.
    Roll, Jacob
    Department of Electrical Engineering, Linköpings universitet.
    Brun, Anders
    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.
    Knutsson, Hans
    Department for Medical Engineering, Linköpings universitet.
    Andersson, Mats
    Department for Medical Engineering, Linköpings universitet.
    Ljung, Lennart
    Department of Electrical Engineering.
    Direct Weight Optimization Applied to Discontinuous Functions2008In: Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico: IEEE , 2008, p. 117-122Conference paper (Refereed)
    Abstract [en]

    The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.

  • 412.
    Ohlsson, Henrik
    et al.
    Department of Electrical Engineering, Linköpings universitet.
    Rydell, Joakim
    Department of Medical Engineering, Linköpings universitet.
    Brun, Anders
    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.
    Roll, Jacob
    Department of Electrical Engineering, Linköpings universitet.
    Andersson, Mats
    Department of Medical Engingeering, Linköpings universitet.
    Ynnerman, Anders
    Department of Science and Technology, Linköpings universitet.
    Knutsson, Hans
    Department of Medical Engineering, Linköpings universitet.
    Enabling Bio-Feedback Using Real-Time fMRI2008In: Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico: IEEE , 2008, p. 3336-3341Conference paper (Refereed)
    Abstract [en]

    Despite the enormous complexity of the human mind, fMRI techniques are able to partially observe the state of a brain in action. In this paper we describe an experimental setup for real-time fMRI in a bio-feedback loop. One of the main challenges in the project is to reach a detection speed, accuracy and spatial resolution necessary to attain sufficient bandwidth of communication to close the bio-feedback loop. To this end we have banked on our previous work on real-time filtering for fMRI and system identification, which has been tailored for use in the experiment setup.

  • 413. Ohlsson, P
    et al.
    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, Automatic control. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Människa-datorinteraktion.
    Real-time Rendering of Accumulated Snow2004In: Sigrad Conference 2004, 2004, p. 25-32Conference paper (Refereed)
  • 414.
    Olsson, Eva
    et al.
    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.
    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.
    Jansson, Anders
    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.
    Sandblad, Bengt
    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.
    The Windscreen Used as a Display for Navigation Information, An Introductory Study: Technical Report 2002-017, Dept. of Information Technology2002Report (Other (popular scientific, debate etc.))
  • 415.
    Orbert, Curt L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms in 2D for Detection of Object Orientation Using Distance Transformations1993Doctoral thesis, monograph (Other academic)
  • 416.
    Pagani, 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.
    Jacobsson, H
    Salmaso, D
    Ramström, C
    Jonsson, C
    Schnell, PO
    Thurfjell, Lennart
    Lundqvist, Roger
    Wägner, A
    Larsson, SA
    Mapping pathological rCBF in Alzheimer disease and Frontal Lobe Dementia using a standardised brain atlas1999In: European association nuclear medicine congress, Barcelona 9th-13th October 1999, SPRINGER VERLAG , 1999, Vol. 26, no 9, p. OS51 -Conference paper (Other academic)
  • 417.
    Pagani, M
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Jonsson, C
    Lundqvist, R
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Thurfjell, Lennart
    Jacobsson, H
    Larsson, SA
    Comparison between regional 2D and volumetric 3D brain SPECT data evaluation in resting state human brain1998Other (Other academic)
  • 418.
    Pagani, M
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Salmaso, D
    Ramstrom, C
    Jonsson, C
    Lundqvist, R
    Thurfjell, L
    Schnell, PO
    Wagner, A
    Jacobsson, H
    Larsson, SA
    Mapping pathological Tc-99m-d,I-hexamethylpropylene amine oxime uptake in Alzheimer's disease and frontal lobe dementia with SPECT2001In: DEMENTIA AND GERIATRIC COGNITIVE DISORDERS, ISSN 1420-8008, Vol. 12, no 3, p. 177-184Article in journal (Refereed)
    Abstract [en]

    Seventeen patients with probable Alzheimer's disease (AD), 7 patients with frontal lobe dementia (FLD) and 19 control subjects (NOR) were examined by Tc-99m-d,l-hexamethylpropylene amine oxime (Tc-99m-HMPAO) SPECT. Images were standardised in the same 3D

  • 419.
    Pedersen, Finn
    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.
    Creating and Comparing Sets of Principal Component Images1995In: SCIA'9, Swedish Society of Automated Image Analysis , 1995, p. 1155-1164Conference paper (Refereed)
  • 420.
    Pedersen, Finn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Interactive Explorative Analysis of Multivariate Images Using Principal Components1994Doctoral thesis, comprehensive summary (Other academic)
  • 421.
    Pedersen, Finn
    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.
    Andersson, Leif
    Bengtsson, Ewert
    Investigating Preprocessing of Multivariate Images in Combination with Principal Component Analysis1997In: SCIA'97, Pattern Recognition Society of Finland , 1997, p. 479-485Conference paper (Refereed)
  • 422.
    Pedersen, Finn
    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.
    Bengtsson, Ewert
    Nordin, Bo
    An extended strategy for exploratory multivariate image analysis including noise considerations1995In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 9, no 5, p. 389-409Article in journal (Refereed)
  • 423.
    Pettersson, Lars W
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Human-Computer Interaction.
    Jensen, N
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. 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, Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    A Virtual laboratory for Computer Graphics Education2003In: Proceedings of EUROGRAPHICS Conf. 2003, 2003Conference paper (Refereed)
  • 424.
    Pettersson, Lars W
    et al.
    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.
    Lind, Mats
    Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science. 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.
    Spak, U
    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.
    Visualizations of symbols in a horizontal multiple viewer 3D display environment2005In: IEEE Proceedings of the 9th International Conference on Information Visualization, 2005, p. 357-362Conference paper (Refereed)
  • 425.
    Pettersson, Lars Winkler
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Human-Computer Interaction.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. 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, Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Spak, Ulrik
    Collaborative 3D Vizualizations of Geo-Spatial Information for Command and Control2004In: Sigrad 2004, 2004, p. 41-47Conference paper (Refereed)
  • 426.
    Pettersson, Lars Winkler
    et al.
    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.
    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.
    Wesslén, Daniel
    In situ tomographic display for interactive data vizualization2004In: NordiCHI 2004, 2004Conference paper (Refereed)
  • 427.
    Philipson née Ammenberg, Petra
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Environmental Applications of Aquatic Remote Sensing2003Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Many lakes, coastal zones and oceans are directly or indirectly influenced by human activities. Through the outlet of a vast amount of substances in the air and water, we are changing the natural conditions on local and global levels.

    Remote sensing sensors, on satellites or airplanes, can collect image data, providing the user with information about the depicted area, object or phenomenon. Three different applications are discussed in this thesis. In the first part, we have used a bio-optical model to derive information about water quality parameters from remote sensing data collected over Swedish lakes. In the second part, remote sensing data have been used to locate and map wastewater plumes from pulp and paper industries along the east coast of Sweden. Finally, in the third part, we have investigated to what extent satellite data can be used to monitor coral reefs and detect coral bleaching.

    Regardless of application, it is important to understand the limitations of this technique. The available sensors are different and limited in terms of their spatial, spectral, radiometric and temporal resolution. We are also limited with respect to the objects we are monitoring, as the concentration of some substances is too low or the objects are too small, to be identified from space. However, this technique gives us a possibility to monitor our environment, in this case the aquatic environment, with a superior spatial coverage. Other advantages with remote sensing are the possibility of getting updated information and that the data is collected and distributed in digital form and therefore can be processed using computers.

  • 428.
    Philipson, Petra
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindell, Tommy
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Nationell kartering från satellitbilder av strandtyper längs svenska havskusten2004Report (Other scientific)
    Abstract [en]

    Natura 2000 i EUs Art- och habitatdirektiv syftar till att bevara skyddsvärda arter och naturtyper i ett sammanhängande nätverk. Dessa naturtyper och arter ska bevaras genom att identifiera ett urval av områden, som sedan kan skyddas enligt naturvårdslagen. För att kunna göra detta urval, och för andra planeringssyften, är information om existerande naturtyper och markanvändning nödvändig. På uppdrag av naturvårdsverket, har därför hela Sveriges strandlinje klassificerats. Satellitbilder från Landsat-7 ETM+ (Image 2000 basen) har använts, vilket resulterade i en upplösning på 25 meter i den slutgiltiga digitala klassningen. Dessa satellitbilder är geometriskt korrigerade till RT 90 och kan därför jämföras med de svenska allmänna kartorna. Strandlinjen är indelad i fem olika klasser: Klippa, Sten, Sand, Hög vegetation och Låg vegetation. Resultatet, inklusive rapport och den digitala klassningen, kommer att distribueras till alla län av Naturvårdsverket. Ansvarig för projektet på Naturvårdsverket är Cecilia Lindblad.

  • 429.
    Pinidiyaarachchi, Amalka
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Digital Image Analysis of Cells: Applications in 2D, 3D and Time2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Light microscopes are essential research tools in biology and medicine. Cell and tissue staining methods have improved immensely over the years and microscopes are now equipped with digital image acquisition capabilities. The image data produced require development of specialized analysis methods. This thesis presents digital image analysis methods for cell image data in 2D, 3D and time sequences.

    Stem cells have the capability to differentiate into specific cell types. The mechanism behind differentiation can be studied by tracking cells over time. This thesis presents a combined segmentation and tracking algorithm for time sequence images of neural stem cells.The method handles splitting and merging of cells and the results are similar to those achieved by manual tracking.

    Methods for detecting and localizing signals from fluorescence stained biomolecules are essential when studying how they function and interact. A study of Smad proteins, that serve as transcription factors by forming complexes and enter the cell nucleus, is included in the thesis. Confocal microscopy images of cell nuclei are delineated using gradient information, and Smad complexes are localized using a novel method for 3D signal detection. Thus, the localization of Smad complexes in relation to the nuclear membrane can be analyzed. A detailed comparison between the proposed and previous methods for detection of point-source signals is presented, showing that the proposed method has better resolving power and is more robust to noise.

    In this thesis, it is also shown how cell confluence can be measured by classification of wavelet based texture features. Monitoring cell confluence is valuable for optimization of cell culture parameters and cell harvest. The results obtained agree with visual observations and provide an efficient approach to monitor cell confluence and detect necrosis.

    Quantitative measurements on cells are important in both cytology and histology. The color provided by Pap (Papanicolaou) staining increases the available image information. The thesis explores different color spaces of Pap smear images from thyroid nodules, with the aim of finding the representation that maximizes detection of malignancies using color information in addition to quantitative morphological parameters.

    The presented methods provide useful tools for cell image analysis, but they can of course also be used for other image analysis applications.

    List of papers
    1. Seeded watersheds for combined segmentation and tracking
    Open this publication in new window or tab >>Seeded watersheds for combined segmentation and tracking
    2005 (English)In: Image Analysis and Processing – ICIAP 2005, Springer Berlin / Heidelberg , 2005, Vol. 3617, p. 336-343Chapter in book (Other academic)
    Abstract [en]

    Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.

    Place, publisher, year, edition, pages
    Springer Berlin / Heidelberg, 2005
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 3617/2005
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-98013 (URN)10.1007/11553595_41 (DOI)978-3-540-28869-5 (ISBN)
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2018-01-13Bibliographically approved
    2. A detailed analysis of 3D subcellular signal localization
    Open this publication in new window or tab >>A detailed analysis of 3D subcellular signal localization
    Show others...
    2009 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 75A, no 4, p. 319-328Article in journal (Refereed) Published
    Abstract [en]

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

    Keywords
    3D image analysis, fluorescence signal segmentation, subcellular positioning, Smad detection
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-98014 (URN)10.1002/cyto.a.20663 (DOI)000264513800006 ()
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2018-01-13Bibliographically approved
    3. Robust signal detection in 3D fluorescence microscopy
    Open this publication in new window or tab >>Robust signal detection in 3D fluorescence microscopy
    2010 (English)In: Cytometry. Part A, ISSN 1552-4922, Vol. 77A, no 1, p. 86-96Article in journal (Refereed) Published
    Abstract [en]

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

    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:uu:diva-98015 (URN)10.1002/cyto.a.20795 (DOI)000273384700011 ()
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2011-11-04Bibliographically approved
    4. Wavelet based estimation on cell confluence
    Open this publication in new window or tab >>Wavelet based estimation on cell confluence
    Manuscript (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-98016 (URN)
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2010-01-13Bibliographically approved
    5. On color spaces for cytology
    Open this publication in new window or tab >>On color spaces for cytology
    2007 (English)In: SSBA 2007, Symposium i bildanalys i Linköping 14-15 mars 2007, 2007Conference paper, Published paper (Refereed)
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-98017 (URN)
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2018-01-13Bibliographically approved
  • 430.
    Pinidiyaarachchi, Amalka
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Alahakoon, P.M.K.
    Ratnatunga, N.V.I.
    Feasibility of using digital image processing in the assessment of cytology smears2006In: 8th International Information Technology Conference (IITC 2006), University of Colombo School of Computing (UCSC), Colombo, Sri Lanka., 2006Conference paper (Other scientific)
  • 431.
    Pinidiyaarachchi, Amalka
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Claesson, Hans
    Wählby, Crolina
    Wavelet based estimation on cell confluenceManuscript (Other academic)
  • 432.
    Pinidiyaarachchi, Amalka
    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.
    Göransson, Jenny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Gonzalez-Rey, Carlos
    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.
    Melin, Jonas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
    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.
    Bengtsson, Ewert
    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, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Digital image processing for multiplexing of single molecule detection2005In: Medicinteknikdagarna: Stockholm/Södertälje September 27-28, 2005, 2005Conference paper (Other academic)
    Abstract [en]

    Using padlock and proximity probing techniques, individual molecular identification events are converted to long DNA molecules, carrying repeated sequence motifs used for identification of the detected molecules. We show that identification events can be amplified using rolling circle replication, and randomly attached to a surface for repeated access by identification probes. Repeated hybridization with detection probes carrying fluorescing nano-crystals (quantum dots) of varying spectral properties opens the possibility to search for large numbers of different identification events simultaneously. Methods for digital image processing of the resulting multi-spectral data include spatial as well as spectral data clustering. Spatial data processing includes registration of images from repeated hybridization events as well as delineation of clustered reporter events. Spectral data processing and analysis includes classification of spectral data into groups of either pre-defined or unknown patterns representing different molecular identification events.

  • 433.
    Pinidiyaarachchi, Amalka
    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.
    On color spaces for cytology2007In: SSBA 2007, Symposium i bildanalys i Linköping 14-15 mars 2007, 2007Conference paper (Refereed)
  • 434.
    Pinidiyaarachchi, Amalka
    et al.
    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.
    Seeded watersheds for combined segmentation and tracking2005In: Image Analysis and Processing – ICIAP 2005, Springer Berlin / Heidelberg , 2005, Vol. 3617, p. 336-343Chapter in book (Other academic)
    Abstract [en]

    Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.

  • 435.
    Pinidiyaarachchi, Amalka
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Zieba, Agata
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Molecular tools.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Pardali, Katerina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Molecular tools.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    A detailed analysis of 3D subcellular signal localization2009In: Cytometry Part A, ISSN 1552-4922, Vol. 75A, no 4, p. 319-328Article in journal (Refereed)
    Abstract [en]

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

  • 436.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Towards Automatic Quantification of Immunohistochemistry Using Colour Image Analysis1998Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Quantification of the proportions of specifically stained regions in images is of significant interest in a growing number of biomedical applications. These applications includes histology and cytology where quantification of various stainings performed on histological tissue sections, smears, imprints etc. is of utmost importance. Through the use of special stains biological components of interest can be given a specific colour. Qualitatively this can be evaluated visually as the presence of a specific colour. But to perform a quantitative evaluation the number of stained cell nuclei and/or the proportion of specimen area that has been stained needs to be measured. Pure visual estimates of this provide very crude results with poor inter- and intraobserver reproducibility. For this purpose computerised image analysis based methods are needed. The methods presented in this thesis aim to make the quantification objective and reproducible.

    A new supervised method for computing a pixelwise box classifier has been developed. The resulting classifier can be applied to images of the same type as the training image as long as the lighting conditions have not been changed. The main advantage of this method is that time will be saved if there are many similar images to classify, since box/classification is a fast method.

    In order to reduce user interaction, automatic methods for classification, based on more specific knowledge about the images, were developed. These methods include automatic classification of two types of roundish objects, e.g. cell nuclei, on a lighter background, first without, and then with the help of reference images of external cultured cells stained together with the specimen. A method for automatic segmentation of dark thin structures, e.g. microvessels, has been developed as well.

    A characteristic of all these methods is that they are implemented as a sequence of single colour band operations, instead of multiband operations. The purpose of this is to make the operations simple and efficient.

  • 437.
    Ranefall, Petter
    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.
    Bengtsson, Ewert
    Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis1997Other (Other academic)
  • 438.
    Ranefall, Petter
    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
    Nordin, Bo
    Bengtsson, Ewert
    A new method for segmentation of colour images applied to immunohistochemically stained cell nuclei1997In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 15, no 3, p. 145-156Article in journal (Refereed)
    Abstract [en]

    A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a

  • 439.
    Ranefall, Petter
    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.
    Nordin, Bo
    Bengtsson, Ewert
    A New Method for Creating a Pixelwise Box classifier for Colour Images1997In: Machine Graphics and Vision, ISSN 1230-0535, Vol. 6, no 3, p. 305-323Article in journal (Refereed)
  • 440.
    Ranefall, Petter
    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.
    Nordin, Bo
    Bengtsson, Ewert
    Finding Facial Features Using an HLS Colour Space1995In: 8th International Conference on Image Analysis and Processing, Springer Verlag , 1995, p. 191-196Conference paper (Refereed)
  • 441.
    Ranefall, Petter
    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.
    Wester, Kenneth
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, Ann-Catrin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Automatic quantification of immunohistochemically stained cell nuclei based on standard reference cells1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, p. 111-23Article in journal (Refereed)
    Abstract [en]

    A fully automatic method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions, is presented. Agarose embedded cultured fibroblasts were fixed, paraffin embedded and sectioned at 4 microm. They were then stained together with 4 microm sections of the test specimen obtained from bladder cancer material. A colour based classifier is automatically computed from the control cells. The method was tested on formalin fixed paraffin embedded tissue section material, stained with monoclonal antibodies against the Ki67 antigen and cyclin A protein. Ki67 staining results in a detailed nuclear texture with pronounced nucleoli and cyclin A staining is obtained in a more homogeneously distributed pattern. However, different staining patterns did not seem to influence labelling index quantification, and the sensitivity to variations in light conditions and choice of areas within the control population was low. Thus, the technique represents a robust and reproducible quantification method. In tests measuring proportions of stained area an average standard deviation of about 1.5% for the same field was achieved when classified with classifiers created from different control samples.

  • 442.
    Ranefall, Petter
    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.
    Wester, Kenneth
    Bengtsson, Ewert
    Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 16, no 1, p. 29-43Article in journal (Refereed)
    Abstract [en]

    A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects

  • 443.
    Ranefall, Petter
    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.
    Wester, Kenneth
    Busch, Christer
    Malmström, Per-Uno
    Bengtsson, Ewert
    Automatic quantification of microvessels using unsupervised image analysis1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, p. 83-92Article in journal (Refereed)
    Abstract [en]

    An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram. To be able

  • 444.
    Razifar, Pasha
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Novel Approaches for Application of Principal Component Analysis on Dynamic PET Images for Improvement of Image Quality and Clinical Diagnosis2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Positron Emission Tomography, PET, can be used for dynamic studies in humans. In such studies a selected part of the body, often the whole brain, is imaged repeatedly after administration of a radiolabelled tracer. Such studies are performed to provide sequences of images reflecting the tracer’s kinetic behaviour, which may be related to physiological, biochemical and functional properties of tissues. This information can be obtained by analyzing the distribution and kinetic behaviour of the administered tracers in different regions, tissues and organs. Each image in the sequence thus contains part of the kinetic information about the administered tracer.

    Several factors make analysis of PET images difficult, such as a high noise magnitude and correlation between image elements in conjunction with a high level of non-specific binding to the target and a sometimes small difference in target expression between pathological and healthy regions. It is therefore important to understand how these factors affect the derived quantitative measurements when using different methods such as kinetic modelling and multivariate image analysis.

    In this thesis, a new method to explore the properties of the noise in dynamic PET images was introduced and implemented. The method is based on an analysis of the autocorrelation function of the images. This was followed by proposing and implementing three novel approaches for application of Principal Component Analysis, PCA, on dynamic human PET studies. The common underlying idea of these approaches was that the images need to be normalized before application of PCA to ensure that the PCA is signal driven, not noise driven. Different ways to estimate and correct for the noise variance were investigated. Normalizations were carried out Slice-Wise (SW), for the whole volume at once, and in both image domain and sinogram domain respectively. We also investigated the value of masking out and removing the area outside the brain for the analysis.

    The results were very encouraging. We could demonstrate that for phantoms as well as for real image data, the applied normalizations allow PCA to reveal the signal much more clearly than what can be seen in the original image data sets. Using our normalizations, PCA can thus be used as a multivariate analysis technique that without any modelling assumptions can separate important kinetic information into different component images. Furthermore, these images contained optimized signal to noise ratio (SNR), low levels of noise and thus showed improved quality and contrast. This should allow more accurate visualization and better precision in the discrimination between pathological and healthy regions. Hopefully this can in turn lead to improved clinical diagnosis.

    List of papers
    1. Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function
    Open this publication in new window or tab >>Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function
    Show others...
    2005 In: BMC Medical Imaging, Vol. 5, no 3Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93687 (URN)
    Available from: 2005-11-11 Created: 2005-11-11Bibliographically approved
    2. Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM
    Open this publication in new window or tab >>Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM
    Show others...
    2005 (English)In: BMC Medical Imaging, Vol. 5, no 5Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93688 (URN)
    Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2010-03-01Bibliographically approved
    3. Performance of PCA and ICA with respect to signal extraction from noisy PET data: a study on computer simulated images
    Open this publication in new window or tab >>Performance of PCA and ICA with respect to signal extraction from noisy PET data: a study on computer simulated images
    Show others...
    (English)In: BMC Nuclear MedicineArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93689 (URN)
    Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2010-03-01Bibliographically approved
    4. PCA with Pre-normalization Improves Image Quality in PET Studies of Amyloid Deposits in Alzheimer’s Disease
    Open this publication in new window or tab >>PCA with Pre-normalization Improves Image Quality in PET Studies of Amyloid Deposits in Alzheimer’s Disease
    Show others...
    (English)In: Journal of Nuclear MedicineArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93690 (URN)
    Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2010-03-01Bibliographically approved
    5. A new Application of Pre-normalized Principal Component Analysis for Improvement of Image Quality and Clinical Diagnosis in Human Brain PET Studies
    Open this publication in new window or tab >>A new Application of Pre-normalized Principal Component Analysis for Improvement of Image Quality and Clinical Diagnosis in Human Brain PET Studies
    Show others...
    (English)In: IEEE Transaction on Medical ImagingArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93691 (URN)
    Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2010-03-01Bibliographically approved
    6. Volume-Wise Application of Principal Component Analysis on Masked Dynamic PET Data in Sinogram Domain
    Open this publication in new window or tab >>Volume-Wise Application of Principal Component Analysis on Masked Dynamic PET Data in Sinogram Domain
    Show others...
    2006 (English)In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 53, no 5, p. 2759-2768Article in journal (Refereed) Published
    Abstract [en]

    Most of the methods used for analyzing PET data are applied in the spatial domain (image domain), in which reconstructed images contain all different types of effects and errors caused by the reconstruction algorithm such as correlation in-between pixels, correlations in-between frames, and streak-artifacts. In this paper, we have investigated a new, pixel wise, noise prenormalization method used for transformation of input data followed by volume-wise application of principal component analysis (PCA) on masked dynamic PET data in the sinogram domain. We are aiming to improve the performance of PCA and to provide images with improved quality and signal extraction. We compare the performance of PCA and the image quality obtained with the new method with previously published approaches. The results show improvement of performance of PCA with respect to, image quality, signal extraction, precision, and visualization.

    Keywords
    masked dynamic data, pixel wise noise prenormalization, positron emission tomography (PET), principal component analysis (PCA), sinogram domain, volume-wise
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-93692 (URN)10.1109/TNS.2006.878008 (DOI)000241367100041 ()
    Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2018-01-13Bibliographically approved
  • 445.
    Razifar, Pasha
    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.
    Axelsson, Jan
    Schneider, Harald
    Långström, Bengt
    Bengtsson, Ewert
    Bergström, Mats
    A new Application of Pre-normalized Principal Component Analysis for Improvement of Image Quality and Clinical Diagnosis in Human Brain PET StudiesIn: IEEE Transaction on Medical ImagingArticle in journal (Refereed)
  • 446.
    Razifar, Pasha
    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.
    Axelsson, Jan
    Uppsala Imanet.
    Schneider, Harald
    Uppsala Imanet.
    Långström, Bengt
    Uppsala Imanet. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    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.
    Bergström, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies - Clinical brain studies using [C-11]-GR205171, [C-11]-L-deuterium-deprenyl, [C-11]-5-hydroxy-L-tryptophan, [C-11]-L-DOPA and Pittsburgh Compound-B2006In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 33, no 2, p. 588-598Article in journal (Refereed)
    Abstract [en]

    Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.

  • 447.
    Razifar, Pasha
    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.
    Axelsson, Jan
    Schneider, Harald
    Långström, Bengt
    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.
    Bergström, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Volume-Wise Application of Principal Component Analysis on Masked Dynamic PET Data in Sinogram Domain2006In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 53, no 5, p. 2759-2768Article in journal (Refereed)
    Abstract [en]

    Most of the methods used for analyzing PET data are applied in the spatial domain (image domain), in which reconstructed images contain all different types of effects and errors caused by the reconstruction algorithm such as correlation in-between pixels, correlations in-between frames, and streak-artifacts. In this paper, we have investigated a new, pixel wise, noise prenormalization method used for transformation of input data followed by volume-wise application of principal component analysis (PCA) on masked dynamic PET data in the sinogram domain. We are aiming to improve the performance of PCA and to provide images with improved quality and signal extraction. We compare the performance of PCA and the image quality obtained with the new method with previously published approaches. The results show improvement of performance of PCA with respect to, image quality, signal extraction, precision, and visualization.

  • 448.
    Razifar, Pasha
    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.
    Blomquist, Gunnar
    Engler, Henry
    Ringheim, Anna
    Schneider, Harald
    Långström, Bengt
    Bengtsson, Ewert
    Bergström, Mats
    PCA with Pre-normalization Improves Image Quality in PET Studies of Amyloid Deposits in Alzheimer’s DiseaseIn: Journal of Nuclear MedicineArticle in journal (Refereed)
  • 449.
    Razifar, Pasha
    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.
    Engler, Henry
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Blomquist, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Oncology, Radiology and Clinical Immunology.
    Ringheim, Anna
    Estrada, Sergio
    Langström, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Biochemistry and Organic Chemistry.
    Bergström, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Principal component analysis with pre-normalization improves the signal-to-noise ratio and image quality in positron emission tomography studies of amyloid deposits in Alzheimer's disease2009In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 54, no 11, p. 3595-3612Article in journal (Refereed)
    Abstract [en]

    This study introduces a new approach for the application of principal component analysis (PCA) with pre-normalization on dynamic positron emission tomography (PET) images. These images are generated using the amyloid imaging agent N-methyl [C-11]2-(4'-methylaminophenyl)-6-hydroxybenzothiazole ([C-11]PIB) in patients with Alzheimer's disease (AD) and healthy volunteers (HVs). The aim was to introduce a method which, by using the whole dataset and without assuming a specific kinetic model, could generate images with improved signal-to-noise and detect, extract and illustrate changes in kinetic behavior between different regions in the brain. Eight AD patients and eight HVs from a previously published study with [C-11] PIB were used. The approach includes enhancement of brain regions where the kinetics of the radiotracer are different from what is seen in the reference region, pre-normalization for differences in noise levels and removal of negative values. This is followed by slice-wise application of PCA (SW-PCA) on the dynamic PET images. Results obtained using the new approach were compared with results obtained using reference Patlak and summed images. The new approach generated images with good quality in which cortical brain regions in AD patients showed high uptake, compared to cerebellum and white matter. Cortical structures in HVs showed low uptake as expected and in good agreement with data generated using kinetic modeling. The introduced approach generated images with enhanced contrast and improved signal-to-noise ratio (SNR) and discrimination power (DP) compared to summed images and parametric images. This method is expected to be an important clinical tool in the diagnosis and differential diagnosis of dementia.

  • 450.
    Razifar, Pasha
    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.
    Engler, Henry
    Ringheim, Anna
    Estrada, Sergio
    Wall, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Section of Nuclear Medicine and PET.
    Långström, Bengt
    An automated method for delineating a reference region using masked volumewise principal-component analysis in 11C-PIB PET2009In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, E-ISSN 1535-5675, Vol. 37, no 1, p. 38-44Article in journal (Refereed)
    Abstract [en]

    Kinetic modeling using a reference region is a common method for the analysis of dynamic PET studies. Available methods for outlining regions of interest representing reference regions are usually time-consuming and difficult and tend to be subjective; therefore, MRI is used to help physicians and experts to define regions of interest with higher precision. The current work introduces a fast and automated method to delineate the reference region of images obtained from an N-methyl-(11)C-2-(4'-methylaminophenyl)-6-hydroxy-benzothiazole ((11)C-PIB) PET study on Alzheimer disease patients and healthy controls using a newly introduced masked volumewise principal-component analysis.

    METHODS: The analysis was performed on PET studies from 22 Alzheimer disease patients (baseline, follow-up, and test/retest studies) and 4 healthy controls, that is, a total of 26 individual scans. The second principal-component images, which illustrate the kinetic behavior of the tracer in gray matter of the cerebellar cortex, were used as input data for automatic delineation of the reference region. To study the variation associated with the manual and proposed automatic methods, we defined the reference region repeatedly.

    RESULTS: As expected, the automatic method showed no variation whereas the manual method varied significantly on repetition. Furthermore, the automatic method was significantly faster, more robust, and less biased.

    CONCLUSION: The automatic method is helpful in the delineation of the reference region of (11)C-PIB PET studies of the human brain and is much faster and more precise than manual delineation.

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