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  • 1.
    Brun, Anders
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Centre for Image Analysis, SLU, Uppsala, Sweden.
    Martin-Fernandez, Marcos
    Universidad de Valladolid Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática Spain.
    Acar, Burac
    Boğaziçi University 5 Electrical & Electronics Engineering Department Istanbul Turkey.
    Munoz-Moreno, Emma
    Universidad de Valladolid Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática Spain.
    Cammoun, Leila
    Signal Processing Institute (ITS), Ecole Polytechnique Fédérale Lausanne (EPFL) Lausanne Switzerland.
    Sigfridsson, Andreas
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Center for Technology in Medicine, Dept. Señales y Comunicaciones, University of Las Palmas de Gran Canaria, Spain.
    Sosa-Cabrera, Dario
    Center for Technology in Medicine, Dept. Señales y Comunicaciones, University of Las Palmas de Gran Canaria, Spain.
    Svensson, Björn
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Herberthson, Magnus
    Linköpings universitet, Matematiska institutionen, Tillämpad matematik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Similar Tensor Arrays - A Framework for Storage of Tensor Array Data2009Inngår i: Tensors in Image Processing and Computer Vision / [ed] Santiago Aja-Fern´andez, Rodrigo de Luis Garc´ıa, Dacheng Tao, Xuelong Li, Springer Science+Business Media B.V., 2009, 1, s. 407-428Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    This chapter describes a framework for storage of tensor array data, useful to describe regularly sampled tensor fields. The main component of the framework, called Similar Tensor Array Core (STAC), is the result of a collaboration between research groups within the SIMILAR network of excellence. It aims to capture the essence of regularly sampled tensor fields using a minimal set of attributes and can therefore be used as a “greatest common divisor” and interface between tensor array processing algorithms. This is potentially useful in applied fields like medical image analysis, in particular in Diffusion Tensor MRI, where misinterpretation of tensor array data is a common source of errors. By promoting a strictly geometric perspective on tensor arrays, with a close resemblance to the terminology used in differential geometry, (STAC) removes ambiguities and guides the user to define all necessary information. In contrast to existing tensor array file formats, it is minimalistic and based on an intrinsic and geometric interpretation of the array itself, without references to other coordinate systems.

  • 2.
    Brun, Anders
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Svensson, Björn
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Westin, Carl-Fredrik
    Herberthson, Magnus
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Matematiska institutionen, Tillämpad matematik.
    Wrangsjö, Andreas
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Filtering Vector-Valued Images using Importance Sampling2007Inngår i: Proceedings of the {SSBA} Symposium on Image Analysis,2007, 2007Konferansepaper (Annet vitenskapelig)
  • 3.
    Brun, Anders
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Svensson, Björn
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Westin, Carl-Fredrik
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Herberthson, Magnus
    Linköpings universitet, Matematiska institutionen, Tillämpad matematik. Linköpings universitet, Tekniska högskolan.
    Wrangsjö, Andreas
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Using Importance Sampling for Bayesian Feature Space Filtering2007Inngår i: Proceedings of the 15th Scandinavian conference on image analysis / [ed] Kjær Bjarne Ersbøll and Kim Steenstrup Pedersen, Berlin, Heidelberg: Springer-Verlag , 2007, s. 818-827Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a one-pass framework for filtering vector-valued images and unordered sets of data points in an N-dimensional feature space. It is based on a local Bayesian framework, previously developed for scalar images, where estimates are computed using expectation values and histograms. In this paper we extended this framework to handle N-dimensional data. To avoid the curse of dimensionality, it uses importance sampling instead of histograms to represent probability density functions. In this novel computational framework we are able to efficiently filter both vector-valued images and data, similar to e.g. the well-known bilateral, median and mean shift filters.

  • 4.
    Langer, Max
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Svensson, Björn
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Brun, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Design of fast multidimensional filters using genetic algorithms2005Inngår i: Applications of Evolutionary Computing: EvoWorkkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC Lausanne, Switzerland, March 30 - April 1, 2005 Proceedings, Springer Berlin/Heidelberg, 2005, s. 366-375Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A method for designing fast multidimensional filters using genetic algorithms is described. The filter is decomposed into component filters where coefficients can be sparsely scattered using filter networks. Placement of coefficients in the filters is done by genetic algorithms and the resulting filters are optimized using an alternating least squares approach. The method is tested on a 2-D quadrature filter and the method yields a higher quality filter in terms of weighted distortion compared to other efficient implementations that require the same ammount of computations to apply. The resulting filter also yields lower weighted distortion than the full implementation.

  • 5.
    Svensson, Björn
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement2008Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Filtering is a fundamental operation in image science in general and in medical image science in particular. The most central applications are image enhancement, registration, segmentation and feature extraction. Even though these applications involve non-linear processing a majority of the methodologies available rely on initial estimates using linear filters. Linear filtering is a well established cornerstone of signal processing, which is reflected by the overwhelming amount of literature on finite impulse response filters and their design.

    Standard techniques for multidimensional filtering are computationally intense. This leads to either a long computation time or a performance loss caused by approximations made in order to increase the computational efficiency. This dissertation presents a framework for realization of efficient multidimensional filters. A weighted least squares design criterion ensures preservation of the performance and the two techniques called filter networks and sub-filter sequences significantly reduce the computational demand.

    A filter network is a realization of a set of filters, which are decomposed into a structure of sparse sub-filters each with a low number of coefficients. Sparsity is here a key property to reduce the number of floating point operations required for filtering. Also, the network structure is important for efficiency, since it determines how the sub-filters contribute to several output nodes, allowing reduction or elimination of redundant computations.

    Filter networks, which is the main contribution of this dissertation, has many potential applications. The primary target of the research presented here has been local structure analysis and image enhancement. A filter network realization for local structure analysis in 3D shows a computational gain, in terms of multiplications required, which can exceed a factor 70 compared to standard convolution. For comparison, this filter network requires approximately the same amount of multiplications per signal sample as a single 2D filter. These results are purely algorithmic and are not in conflict with the use of hardware acceleration techniques such as parallel processing or graphics processing units (GPU). To get a flavor of the computation time required, a prototype implementation which makes use of filter networks carries out image enhancement in 3D, involving the computation of 16 filter responses, at an approximate speed of 1MVoxel/s on a standard PC.

    Delarbeid
    1. On Geometric Transformations of Local Structure Tensors
    Åpne denne publikasjonen i ny fane eller vindu >>On Geometric Transformations of Local Structure Tensors
    Manuskript (Annet vitenskapelig)
    Identifikatorer
    urn:nbn:se:liu:diva-13022 (URN)
    Tilgjengelig fra: 2008-03-13 Laget: 2008-03-13 Sist oppdatert: 2010-01-13
    2. Estimation of Non-Cartesian Local Structure Tensor Fields
    Åpne denne publikasjonen i ny fane eller vindu >>Estimation of Non-Cartesian Local Structure Tensor Fields
    2007 (engelsk)Inngår i: Image Analysis: 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007 / [ed] Bjarne Kjær Ersbøll, Kim Steenstrup Pedersen, Springer Berlin/Heidelberg, 2007, Vol. 4522/2007, s. 948-957Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    In medical imaging, signals acquired in non-Cartesian coordinate systems are common. For instance, CT and MRI often produce significantly higher resolution within scan planes, compared to the distance between two adjacent planes. Even oblique sampling occurs, by the use of gantry tilt. In ultrasound imaging, samples are acquired in a polar coordinate system, which implies a spatially varying metric.

    In order to produce a geometrically correct image, signals are generally resampled to a Cartesian coordinate system. This paper concerns estimation of local structure tensors directly from the non-Cartesian coordinate system, thus avoiding deteriorated signal and noise characteristics caused by resampling. In many cases processing directly in the warped coordinate system is also less time-consuming. A geometrically correct tensor must obey certain transformation rules originating from fundamental differential geometry. Subsequently, this fact also affects the tensor estimation. As the local structure tensor is estimated using filters, a change of coordinate system also change the shape of the spatial support of these filters. Implications and limitations brought on by sampling require the filter design criteria to be adapted to the coordinate system.

    sted, utgiver, år, opplag, sider
    Springer Berlin/Heidelberg, 2007
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 4522
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-13023 (URN)10.1007/978-3-540-73040-8_96 (DOI)000247364000096 ()978-3-540-73039-2 (ISBN)978-3-540-73040-8 (ISBN)
    Konferanse
    15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007
    Tilgjengelig fra: 2008-03-13 Laget: 2008-03-13 Sist oppdatert: 2018-02-15bibliografisk kontrollert
    3. Efficient 3-D Adaptive Filtering for Medical Image Enhancement
    Åpne denne publikasjonen i ny fane eller vindu >>Efficient 3-D Adaptive Filtering for Medical Image Enhancement
    2006 (engelsk)Inngår i: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., IEEE , 2006, s. 996-999Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Tensor based orientation adaptive filtering, an explicit methodology for anisotropic filtering, constitutes a flexible framework for medical image enhancement. The technique features post-filtering steerability and allows user interaction and direct control over the high-frequency contents of the signal. A new class of filters for local structure analysis together with filter networks significantly lowers the complexity to meet the requirements of computation time for clinical use, while maintaining accuracy. In this paper the technique is applied to low-dose CT-images, magnetic resonance angiography and T2-weighted MRI.

    sted, utgiver, år, opplag, sider
    IEEE, 2006
    Serie
    International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-13024 (URN)10.1109/ISBI.2006.1625088 (DOI)000244446000252 ()0-7803-9576-X (ISBN)
    Konferanse
    3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 6-9 April 2006, Arlington, VA, USA
    Tilgjengelig fra: 2008-03-13 Laget: 2008-03-13 Sist oppdatert: 2014-01-31bibliografisk kontrollert
    4. Approximate Spectral Factorization for Design of Efficient Sub-Filter Sequences
    Åpne denne publikasjonen i ny fane eller vindu >>Approximate Spectral Factorization for Design of Efficient Sub-Filter Sequences
    2008 (engelsk)Manuskript (preprint) (Annet (populærvitenskap, debatt, mm))
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-13025 (URN)
    Tilgjengelig fra: 2008-03-13 Laget: 2008-03-13 Sist oppdatert: 2015-06-02
    5. Filter Networks for Efficient Estimation of Local 3-D Structure
    Åpne denne publikasjonen i ny fane eller vindu >>Filter Networks for Efficient Estimation of Local 3-D Structure
    2005 (engelsk)Inngår i: IEEE International Conference on Image Processing (ICIP). 11-14 Sept, Genoa, Italy, 2005, Vol. 3, s. 573-576Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Linear filtering is a fundamental operation in signal processing, but for multidimensional signals the practical use is severely limited by the computer power available. Decomposition of filters into a layered structure of sparse subfilters, i.e. a filter network, significantly reduces the number of multiplications required for each data sample. A filter network, here used for phase invariant estimation of local 3-D structure, provides a flexible solution for linear filtering, especially suited for applying a set of filters on signals of higher dimensionality. The filter network presented, is twice as efficient as convolution based on the fast Fourier transform (FFT) and outperforms standard convolution by a factor exceeding 50 in terms of multiplications and additions performed.

    Emneord
    filter design, filter network, sparse filters, efficient filtering, local structure
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-13026 (URN)10.1109/ICIP.2005.1530456 (DOI)
    Tilgjengelig fra: 2008-03-13 Laget: 2008-03-13 Sist oppdatert: 2013-08-28
    6. A Graph Representation of Filter Networks
    Åpne denne publikasjonen i ny fane eller vindu >>A Graph Representation of Filter Networks
    2005 (engelsk)Inngår i: Scandinavian Conference on Image Analysis (SCIA). Joensuu, Finland, 2005, s. 1086-1095Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Filter networks, i.e. decomposition of a filter set into a layered structure of sparse subfilters has been proven successful for e.g. efficient convolution using finite impulse response filters. The efficiency is due to the significantly reduced number of multiplications and additions per data sample that is required. The computational gain is dependent on the choice of network structure and the graph representation compactly incorporates the network structure in the design objectives. Consequently the graph representation forms a framework for searching the optimal network structure. It also removes the requirement of a layered structure, at the cost of a less compact representation.

    Emneord
    filters, orientation_frequency_phase, tensors, CMIV
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-13027 (URN)
    Tilgjengelig fra: 2008-03-13 Laget: 2008-03-13 Sist oppdatert: 2013-08-28
  • 6.
    Svensson, Björn
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Fast multi-dimensional filter networks: design, optimization and implementation2006Licentiatavhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    As the title implies a fast filter network is a structure of filters, designed for efficient computation of a set of multi-dimensional filters. The efficiency is due to decomposition of multi-dimensional filter sets into a structure of smaller sparse filters called sub-filters. The structure used, forms a directed acyclic graph which allows the sub-filters to contribute to several output nodes of the networks, i.e. several filters in the set.

    The use of filter networks involves non-trivial design, i.e. choosing the network structure and optimizing each sub-filter. In this thesis, the filter networks are constrained to perform linear filtering, one of the most fundamental operation in signal processing. The design problem associated with filter networks is described and solutions found has been implemented for extracting features like signal orientation, local frequency, local phase, local bandwidth and degree of anisotropy from volumetric data.

    Filter networks has many potential applications and the primary target in this thesis has been local structure analysis. The implemented filter networks show a computational gain of factors exceeding 50 for estimation of local 3-D structure compared to standard convolution. As a proof of concept showing use in medical applications, filter networks for enhancement of medical 3-D data is presented.

  • 7.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    A Graph Representation of Filter Networks2005Inngår i: Scandinavian Conference on Image Analysis (SCIA). Joensuu, Finland, 2005, s. 1086-1095Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Filter networks, i.e. decomposition of a filter set into a layered structure of sparse subfilters has been proven successful for e.g. efficient convolution using finite impulse response filters. The efficiency is due to the significantly reduced number of multiplications and additions per data sample that is required. The computational gain is dependent on the choice of network structure and the graph representation compactly incorporates the network structure in the design objectives. Consequently the graph representation forms a framework for searching the optimal network structure. It also removes the requirement of a layered structure, at the cost of a less compact representation.

  • 8.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Filter Networks for efficient estimation of local 3-D structure2005Inngår i: Symposium on Image Analysis SSBA,2005, 2005, s. 17-20Konferansepaper (Annet vitenskapelig)
  • 9.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Filter Networks for Efficient Estimation of Local 3-D Structure2005Inngår i: IEEE International Conference on Image Processing (ICIP). 11-14 Sept, Genoa, Italy, 2005, Vol. 3, s. 573-576Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Linear filtering is a fundamental operation in signal processing, but for multidimensional signals the practical use is severely limited by the computer power available. Decomposition of filters into a layered structure of sparse subfilters, i.e. a filter network, significantly reduces the number of multiplications required for each data sample. A filter network, here used for phase invariant estimation of local 3-D structure, provides a flexible solution for linear filtering, especially suited for applying a set of filters on signals of higher dimensionality. The filter network presented, is twice as efficient as convolution based on the fast Fourier transform (FFT) and outperforms standard convolution by a factor exceeding 50 in terms of multiplications and additions performed.

  • 10.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    On Phase-Invariant Structure Tensors and Local Image Metrics2008Konferansepaper (Annet vitenskapelig)
  • 11.
    Svensson, Björn
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Sparse approximation for FIR filter design2006Inngår i: SSBA Symposium on Image Analysis,2006, 2006Konferansepaper (Annet vitenskapelig)
  • 12.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Efficient 3-D Adaptive Filtering for Medical Image Enhancement2006Inngår i: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., IEEE , 2006, s. 996-999Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Tensor based orientation adaptive filtering, an explicit methodology for anisotropic filtering, constitutes a flexible framework for medical image enhancement. The technique features post-filtering steerability and allows user interaction and direct control over the high-frequency contents of the signal. A new class of filters for local structure analysis together with filter networks significantly lowers the complexity to meet the requirements of computation time for clinical use, while maintaining accuracy. In this paper the technique is applied to low-dose CT-images, magnetic resonance angiography and T2-weighted MRI.

  • 13.
    Svensson, Björn
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Radiation dose reduction by efficient 3D image restoration2006Inngår i: Proceedings of the European Congress of Radiology, 2006, 2006Konferansepaper (Fagfellevurdert)
  • 14.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Wiklund, Johan
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Issues on filter networks for efficient convolution2004Inngår i: Proceedings of the Swedish Symposium on Image Analysis (2004), Uppsala, 2004, s. 94-97Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    This paper presents the new project Efficient Convolution Operators for Image Processing of Volumes and Volume Sequences . The project is carried out in collaboration with Context Vision AB.

    By using sequential filtering on 3D and 4D data, the number of nonzero filter coefficients for a desired filter set can be significantly reduced. A sequential convolution structure in combination with a convolver designed for sparse filters is a powerful tool for filtering of multidimensional signals.

    The project mainly concerns the design of filter networks, that approximate a desired filter set, while keeping the computational load as low as possible. This is clearly an optimization problem, but it can be formulated in several different ways due to the complexity.

    The project is in an initial state and the paper focuses on experiences from prior work and discuss possible approaches for the future progress.

  • 15.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Brun, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Estimation of Non-Cartesian Local Structure Tensor Fields2007Inngår i: Image Analysis: 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007 / [ed] Bjarne Kjær Ersbøll, Kim Steenstrup Pedersen, Springer Berlin/Heidelberg, 2007, Vol. 4522/2007, s. 948-957Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In medical imaging, signals acquired in non-Cartesian coordinate systems are common. For instance, CT and MRI often produce significantly higher resolution within scan planes, compared to the distance between two adjacent planes. Even oblique sampling occurs, by the use of gantry tilt. In ultrasound imaging, samples are acquired in a polar coordinate system, which implies a spatially varying metric.

    In order to produce a geometrically correct image, signals are generally resampled to a Cartesian coordinate system. This paper concerns estimation of local structure tensors directly from the non-Cartesian coordinate system, thus avoiding deteriorated signal and noise characteristics caused by resampling. In many cases processing directly in the warped coordinate system is also less time-consuming. A geometrically correct tensor must obey certain transformation rules originating from fundamental differential geometry. Subsequently, this fact also affects the tensor estimation. As the local structure tensor is estimated using filters, a change of coordinate system also change the shape of the spatial support of these filters. Implications and limitations brought on by sampling require the filter design criteria to be adapted to the coordinate system.

  • 16.
    Svensson, Björn
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Brun, Anders
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Geometric Transformations of Local Structure Tensors2006Inngår i: Similar NoE Tensor Workshop,2006, 2006Konferansepaper (Annet vitenskapelig)
  • 17.
    Svensson, Björn
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Brun, Anders
    Centre for Image Analysis, SLU, Uppsala, Sweden.
    Andersson, Mats
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    On Geometric Transformations of Local Structure Tensors2009Inngår i: Tensors in Image Processing and Computer Vision: Part 2 / [ed] S. Aja-Fernandez, R. de Luis Garcia, D. Tao, X. Li, Springer London, 2009, s. 179-193Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The structure of images has been studied for decades and the use of local structure tensor fields appeared during the eighties [3, 14, 6, 9, 11]. Since then numerous varieties of tensors and estimation schemes have been developed. Tensors have for instance been used to represent orientation [7], velocity, curvature [2] and diffusion [19] with applications to adaptive filtering [8], motion analysis [10] and segmentation [17]. Even though sampling in non-Cartesian coordinate system are common, analysis and processing of local structure tensor fields in such systems is less developed. Previous work on local structure in non-Cartesian coordinate systems include [21, 16, 1, 18].

  • 18.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Burdakov, Oleg
    Linköpings universitet, Matematiska institutionen, Optimeringslära. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    A New Approach For Treating Multiple Eextremal Points In Multi-Linear Least Squares Filter Design2007Inngår i: Proceedings of the {SSBA} Symposium on Image Analysis, 2007, 2007, s. 61-64Konferansepaper (Annet vitenskapelig)
  • 19.
    Svensson, Björn
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Burdakov, Oleg
    Linköpings universitet, Matematiska institutionen, Optimeringslära. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Approximate Spectral Factorization for Design of Efficient Sub-Filter Sequences2008Manuskript (preprint) (Annet (populærvitenskap, debatt, mm))
1 - 19 of 19
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