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  • 1.
    Aksoy, Selim
    et al.
    Intelligent Systems Laboratory, University of Washington, Seattle, USA.
    Ming, Ye
    Intelligent Systems Laboratory, University of Washington, Seattle, USA.
    Schauf, Michael L.
    Intelligent Systems Laboratory, University of Washington, Seattle, USA.
    Song, Mingzhou
    Intelligent Systems Laboratory, University of Washington, Seattle, USA.
    Wang, Yalin
    n/a.
    Haralick, Robert M.
    Intelligent Systems Laboratory, University of Washington, Seattle, USA.
    Parker, Jim R.
    University of Calgary, Dept. of Computer Science, Calgary, Canada.
    Pivovarov, Juraj
    University of Calgary, Dept. of Computer Science, Calgary, Canada.
    Royko, Dominik
    University of Calgary, Dept. of Computer Science, Calgary, Canada.
    Sun, Changming
    CSIRO Mathematical and Information Sciences, Australia.
    Farnebäck, Gunnar
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Algorithm Performance Contest2000In: Proceedings. 15th International Conference on Pattern Recognition, 2000: Barcelona, Spain, IEEE , 2000, Vol. 4, p. 870-876Conference paper (Refereed)
    Abstract [en]

    This contest involved the running and evaluation of computer vision and pattern recognition techniques on different data sets with known groundwidth. The contest included three areas; binary shape recognition, symbol recognition and image flow estimation. A package was made available for each area. Each package contained either real images with manual groundtruth or programs to generate data sets of ideal as well as noisy images with known groundtruth. They also contained programs to evaluate the results of an algorithm according to the given groundtruth. These evaluation criteria included the generation of confusion matrices, computation of the misdetection and false alarm rates and other performance measures suitable for the problems. The paper summarizes the data generation for each area and experimental results for a total of six participating algorithms

  • 2.
    Andersson, Mats
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Sandborg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Farnebäck, Gunnar
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Hans, Knutsson
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Adaptiv filtering of 4D-heart CT for image denoising and patient safety2010Conference paper (Other academic)
    Abstract [en]

    The aim of this medical image science project is to increase patient safety in terms of improved image quality and reduced exposure to ionizing radiation in CT. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes. Four-dimensional (4D) medical image data are captured as a time sequence of image volumes. During 4D image acquisition, a 3D image of the patient is recorded at regular time intervals. The resulting data will consequently have three spatial dimensions and one temporal dimension. Increasing the dimensionality of the data impose a major increase the computational demands. The initial linear filtering which is the cornerstone in all adaptive image enhancement algorithms increase exponentially with the dimensionality. On the other hand the potential gain in Signal to Noise Ratio (SNR) also increase exponentially with the dimensionality. This means that the same gain in noise reduction that can be attained by performing the adaptive filtering in 3D as opposed to 2D can be expected to occur once more by moving from 3D to 4D. The initial tests on on both synthetic and clinical 4D images has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. When tuning the parameters for adaptive filtering is extremely important to attain maximal diagnostic value which not necessarily coincide with an an eye pleasing image for a layman. Although this application focus on CT the resulting adaptive filtering methods will be beneficial for a wide range of 3D/4D medical imaging modalities e.g. shorter acquisition time in MRI and improved elimination of noise in 3D or 4D ultrasound datasets.

  • 3.
    Andersson, Thord
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Nordberg, Klas
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    WITAS Project at Computer Vision Laboratory; A status report (Jan 1998)1998In: Proceedings of the SSAB symposium on image analysis: Uppsala, Sweden, 1998, p. 113-116Conference paper (Refereed)
    Abstract [en]

    WITAS will be engaged in goal-directed basic research in the area of intelligent autonomous vehicles and other autonomous systems. In this paper an overview of the project is given together with a presentation of our research interests in the project. The current status of our part in the project is also given.

  • 4.
    Ebbers, Tino
    et al.
    Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Farneback, Gunnar
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Improving Computation of Cardiovascular Relative Pressure Fields From Velocity MRI2009In: JOURNAL OF MAGNETIC RESONANCE IMAGING, ISSN 1053-1807, Vol. 30, no 1, p. 54-61Article in journal (Refereed)
    Abstract [en]

    Purpose: To evaluate a multigrid-based solver for the pressure Poisson equation (PPE) with Galerkin coarsening, which works directly on the specified domain, for the computation of relative pressure fields from velocity MRI data. Materials and Methods: We compared the proposed structure-defined Poisson solver to other popular Poisson solvers working on unmodified rectangular and modified quasirectangular domains using synthetic and in vitro phantoms in which the mathematical solution of the pressure field is known, as well as on in vivo MRI velocity measurements of aortic blood flow dynamics. Results: All three PPE solvers gave accurate results for convex computational domains. Using a rectangular or quasirectangular domain on a more complicated domain, like a c-shape, revealed a systematic underestimation of the pressure amplitudes, while the proposed PPE solver, working directly on the specified domain, provided accurate estimates of the relative pressure fields. Conclusion: Popular iterative approaches with quasirectangular computational domains can lead to significant systematic underestimation of the pressure amplitude. We suggest using a multigrid-based PPE solver with Galerkin coarsening, which works directly on the structure-defined computational domain. This solver provides accurate estimates of the relative pressure fields for both simple and complex geometries with additional significant improvements with respect to execution speed.

  • 5.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Unified Framework for Bases, Frames, Subspace Bases, and Subspace Frames1999In: Proceedings of the 11th Scandinavian Conference on Image Analysis: Kangerlussuaq, Greenland, 1999, p. 341-349Conference paper (Refereed)
    Abstract [en]

    Frame representations (e.g. wavelets) and subspace projections are important tools in many image processing applications. A unified framework for frames and subspace bases, as well as bases and subspace frames, is developed for finite dimensional vector spaces. Dual (subspace) bases and frames are constructed and the theory is generalized to weighted norms and seminorms. It is demonstrated how the framework applies to the cubic facet model, to normalized convolution, and to projection onto second degree polynomials.

  • 6.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Disparity Estimation from Local Polynomial Expansion2001In: Proceedings of the SSAB Symposium on Image Analysis: Norrköping, 2001, p. 77-80Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel disparity estimation algorithm based on local polynomial expansion of the images in a stereo pair. Being a spin-off from work on two-frame motion estimation, it is primarily intended as a proof of concept for some of the underlying ideas. It may, however, be useful on its own as well, since it is very simple and fast. The accuracy still remains to be determined.

  • 7.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Fast and Accurate Motion Estimation using Orientation Tensors and Parametric Motion Models2000In: ICPR15: Barcelona, Spain, IEEE , 2000, Vol. 1, p. 135-139 vol.1Conference paper (Refereed)
    Abstract [en]

    Motion estimation in image sequences is an important step in many computer vision and image processing applications. Several methods for solving this problem have been proposed, but very few manage to achieve a high level of accuracy without sacrificing processing speed. This paper presents a novel motion estimation algorithm, which gives excellent results on both counts. The algorithm starts by computing 3D orientation tensors from the image sequence. These are combined under the constraints of a parametric motion model to produce velocity estimates. Evaluated on the well-known Yosemite sequence, the algorithm shows an accuracy which is substantially better than for previously published methods. Computationally the algorithm is simple and can be implemented by means of separable convolutions, which also makes it fast.

  • 8.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Motion-based Segmentation of Image Sequences using Orientation Tensors1997In: Proceedings of the SSAB Symposium on Image Analysis: Stockholm, 1997, p. 31-35Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of motion-based segmentation of image sequences. One motion estimation algorithm and two segmentation algorithms are presented. The motion estimation is based on 3D orientation tensors and the algorithm can be used to estimate a large class of motion models, including the affine model that is used in the segmentation. The segmentation algorithms are based on a competitive region growing approach.

  • 9.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Orientation Estimation Based on Weighted Projection onto Quadratic Polynomials2000In: Vision, Modeling, and Visualization 2000: proceedings, 2000, p. 89-96Conference paper (Refereed)
    Abstract [en]

    Essentially all Computer Vision strategies require initial computation of orientation structure or motion estimation. Although much work has been invested in this subfield, methods have so far been very computationally demanding and/or not very robust. In this paper we present a novel method for computation of orientation tensors for signals of any dimensionality. The method is based on local weighted least squares approximations of the signal by second degree polynomials. It is shown how this can be implemented very efficiently by means of separable convolutions and that the method gives very accurate orientation estimates. We also introduce the new concept of orientation functionals, of which orientation tensors is a subclass. Finally we demonstrate the critical importance of using a proper weighting function in the local projection of the signal onto polynomials.

  • 10.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Two-Frame Motion Estimation Based on Polynomial Expansion2003In: SCIA13: Gothenburg, Sweden, 2003, p. 363-370Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel two-frame motion estimation algorithm. The first step is to approximate each neighborhood of both frames by quadratic polynomials, which can be done efficiently using the polynomial expansion transform. From observing how an exact polynomial transforms under translation a method to estimate displacement fields from the polynomial expansion coefficients is derived and after a series of refinements leads to a robust algorithm. Evaluation on the Yosemite sequence shows good results.

  • 11.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Two-frame motion estimation based on polynomial expansion2003In: Image Analysis: 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden, June 29 – July 2, 2003 Proceedings / [ed] Josef Bigun, Tomas Gustavsson, Springer Berlin/Heidelberg, 2003, Vol. 2749, p. 363-370Chapter in book (Refereed)
    Abstract [en]

    This paper presents a novel two-frame motion estimation algorithm. The first step is to approximate each neighborhood of both frames by quadratic polynomials, which can be done efficiently using the polynomial expansion transform. From observing how an exact polynomial transforms under translation a method to estimate displacement fields from the polynomial expansion coefficients is derived and after a series of refinements leads to a robust algorithm. Evaluation on the Yosemite sequence shows good results.

  • 12.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Very High Accuracy Velocity Estimation using Orientation Tensors, Parametric Motion, and Simultaneous Segmentation of the Motion Field2001In: Proceedings of the Eighth IEEE International Conference on Computer Vision: Vancouver, Canada, 2001, Vol. I, p. 171-177Conference paper (Refereed)
    Abstract [en]

    In [Farnebäck00] we presented a new velocity estimation algorithm, using orientation tensors and parametric motion models to provide both fast and accurate results. One of the tradeoffs between accuracy and speed was that no attempts were made to obtain regions of coherent motion when estimating the parametric models. In this paper we show how this can be improved by doing a simultaneous segmentation of the motion field. The resulting algorithm is slower than the previous one, but more accurate. This is shown by evaluation on the well-known Yosemite sequence, where already the previous algorithm showed an accuracy which was substantially better than for earlier published methods. This result has now been improved further.

  • 13.
    Farnebäck, Gunnar
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Detection of point-shaped targets1996Report (Other academic)
    Abstract [en]

    This report documents work done at the request of the Swedish Defense Research Establishment. The studied problem is that of detecting point-shaped targets, i.e. targets whose only significant property is that of being very small, in a cluttered environment. Three approaches to the problem have been considered. The first one, based on motion compensation, was rejected at an early stage due to expected problems with robustness and computational demands. The second method, based on background modeling with principal components, turned out successful and has been studied in depth, including discussion of various extensions and improvements of the presented algorithm. Finally, a Wiener filter approach has also turned out successful, including an approximation with separable filters. The methods have been tested on sequences obtained by an IR sensor. While both the two latter approaches work well on the test sequences, the Wiener filter is simpler and computationally less expensive than the background modeling. On the other hand, the background modeling is likely to have better possibilities for extensions and improvements.

  • 14.
    Farnebäck, Gunnar
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Nordberg, Klas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Motion Detection in the WITAS Project2002In: Swedish Symposium on Image Analysis (SSBA): Lund, 2002, p. 99-102Conference paper (Refereed)
    Abstract [en]

    One important problem within the WITAS project is detection of moving objects in aerial images. This paper presents an original method to estimate the displacement between two frames, based on multiscale local polynomial expansions of the images. When the displacement field has been computed, a plane + parallax approach is used to separate moving objects from the camera egomotion.

  • 15.
    Farnebäck, Gunnar
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Efficient computation of the inverse gradient on irregular domains2007In: 2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, IEEE , 2007, p. 2710-2717Conference paper (Other academic)
    Abstract [en]

    The inverse gradient problem, finding a scalar field f with a gradient near a given vector field g on some bounded and connected domain Omega epsilon R(n), can be solved by means of a Poisson equation with inhomogeneous Neumann boundary conditions. We present an elementary derivation of this partial differential equation and an efficient multigrid-based method to numerically compute the inverse gradient on non-rectangular domains. The utility of the method is demonstrated by a range of important medical applications such as phase unwrapping, pressure computation, inverse deformation fields, and fiber bundle tracking.

  • 16.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Harvard Medical School.
    Multi-modal Image Registration Using Polynomial Expansion and Mutual Information2012In: Biomedical Image Registration: Proceedings of the 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012 / [ed] Benoit M. Dawant, Gary E. Christensen, J.Michael Fitzpatrick and Daniel Rueckert, Springer Berlin/Heidelberg, 2012, p. 40-49Chapter in book (Refereed)
    Abstract [en]

    This book constitutes the refereed proceedings of the 5th International Workshop on Biomedical Image Registration, WBIR 2012, held in Nashville, Tennessee, USA, in July 2012. The 20 full papers and 11 poster papers included in this volume were carefully reviewed and selected from 44 submitted papers. They full papers are organized in the following topical sections: multiple image sets; brain; non-rigid anatomy; and frameworks and similarity measures.

  • 17.
    Johansson, Björn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Farnebäck, Gunnar
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    A Theoretical Comparison of Different Orientation Tensors2002In: Proceedings SSAB02 Symposium on Image Analysis,2002, 2002, p. 69-73Conference paper (Other academic)
  • 18.
    Nordberg, Klas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Farnebäck, Gunnar
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Forssén, Per-Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Wiklund, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Vision for a UAV helicopter2002In: International Conference on Intelligent Robots and Systems (IROS), Workshop on Aerial Robotics: Lausanne, Switzerland, 2002Conference paper (Other academic)
    Abstract [en]

    This paper presents and overview of the basic and applied research carried out by the Computer Vision Laboratory, Linköping University, in the WITAS UAV Project. This work includes customizing and redesigning vision methods to fit the particular needs and restrictions imposed by the UAV platform, e.g., for low-level vision, motion estimation, navigation, and tracking. It also includes a new learning structure for association of perception-action activations, and a runtime system for implementation and execution of vision algorithms. The paper contains also a brief introduction to the WITAS UAV Project.

  • 19.
    Nordberg, Klas
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Framework for Estimation of Orientation and Velocity2003In: International Conference on Image Processing (ICIP): Barcelona, Spain, 2003Conference paper (Refereed)
    Abstract [en]

    The paper makes a short presentation of three existing methods for estimation of orientation tensors, the so-called structure tensor, quadrature filter based techniques, and techniques based on approximating a local polynomial model. All three methods can be used for estimating an orientation tensor which in the 3D case can be used for motion estimation. The methods are based on rather different approaches in terms of the underlying signal models. However, they produce more or less similar results which indicates that there should be a common framework for estimation of the tensors. Such a framework is proposed, in terms of a second order mapping from signal to tensor with additional conditions on the mapping. It it also shown that the three methods in principle fall into this framework.

  • 20.
    Nordberg, Klas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Farnebäck, Gunnar
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Estimation of orientation tensors for simple signals by means of second-order filters2005In: Signal Processing: Image Communication, ISSN 0923-5965, Vol. 20, no 6, p. 582-594Article in journal (Refereed)
    Abstract [en]

    Tensors have become a popular tool for representation of local orientation and can be used also for estimation of velocity. A number of computational approaches have been presented for tensor estimation which, however, are difficult to analyze or compare since there has been no common framework in which analysis or comparisons can be made. In this article, we propose such a framework based on second-order filters and show how it applies to three different methods for tensor estimation. The framework contains a few conditions on the filters which are sufficient to obtain correctly oriented rank one tensors for the case of simple signals. It also allows the derivation of explicit expressions for the variation of the tensor across oriented structures which, e.g., can be used to formulate conditions for phase invariance. (c) 2005 Elsevier B.V. All rights reserved.

  • 21.
    Nordberg, Klas
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Rank complement of diagonalizable matrices using polynomial functions2001Report (Other academic)
    Abstract [en]

    This report defines the rank complement of a diagonalizable matrix (i.e. a matrix which can be brought to a diagonal form by means of a change of basis) as the interchange of the range and the null space. Given a diagonalizable matrix A there is in general no unique matrix Ac which has a range equal to the null space of A and a null space equal to the range of A, only matrices of full rank have a unique rank complement; the zero matrix. Consequently, the rank complement operation is not a distinct operation, but rather a characterization of any operation which makes an interchange of the range and the null space. One particular rank complement operation is introduced here, which eventually leads to an implementation of rank complement operations in terms of polynomials in A. The main result is that for each possible rank r of A there is a polynomial in A which evaluates to a matrix Ac which is a rank complement of A. The report provides explicit expressions for matrix polynomials which compute a rank complement of a symmetric matrix. These results are then generalized to the case of diagonalizable matrices. Finally, a Matlab function is described that implements a rank complement operation based on the results derived.

  • 22.
    Rydell, Joakim
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Johansson, Andreas
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Leinard, Olof Dahlqvist
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences.
    Three Dimensional Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging using Inverse Gradient2008In: Proceedings of the International Society for Magnetic Resonance in Medicine annual meeting (ISMRM'08), 2008, p. 1519-Conference paper (Other academic)
    Abstract [en]

    Three dimensional phase sensitive reconstruction on two point Dixon volumes has been implemented with use of the inverse gradient. The results has beencompared with the inverse gradient method in two dimensions as well as with the well established region growing method proposed by Ma. The inversegradient method in 3D is able to unwrap the phase field in uncertain regions where the region growing method and the inverse gradient method in 2D cometo a stop.

  • 23.
    Rydell, Joakim
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Pettersson, Johanna
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Johansson, Andreas
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Department of Medicine and Care, Medical Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sebastien Ourselin and Anthony Maeder, Springer Berlin/Heidelberg, 2007, p. 210-218Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

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