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Svensson, Björn
Publications (10 of 19) Show all publications
Svensson, B., Brun, A., Andersson, M. & Knutsson, H. (2009). On Geometric Transformations of Local Structure Tensors. In: S. Aja-Fernandez, R. de Luis Garcia, D. Tao, X. Li (Ed.), Tensors in Image Processing and Computer Vision: Part 2. Paper presented at Tensors in Image Processing and Computer Vision (pp. 179-193). Paper presented at Tensors in Image Processing and Computer Vision. Springer London
Open this publication in new window or tab >>On Geometric Transformations of Local Structure Tensors
2009 (English)In: Tensors in Image Processing and Computer Vision: Part 2 / [ed] S. Aja-Fernandez, R. de Luis Garcia, D. Tao, X. Li, Springer London, 2009, p. 179-193Chapter in book (Refereed)
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].

Place, publisher, year, edition, pages
Springer London, 2009
Series
Advances in Pattern Recognition, ISSN 1617-7916
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-60131 (URN)10.1007/978-1-84882-299-3_8 (DOI)978-1-84882-298-6 (ISBN)978-1-84882-299-3 (ISBN)
Conference
Tensors in Image Processing and Computer Vision
Funder
Swedish Research CouncilVINNOVA
Available from: 2010-10-06 Created: 2010-10-06 Last updated: 2015-08-19Bibliographically approved
Brun, A., Martin-Fernandez, M., Acar, B., Munoz-Moreno, E., Cammoun, L., Sigfridsson, A., . . . Knutsson, H. (2009). Similar Tensor Arrays - A Framework for Storage of Tensor Array Data (1ed.). In: Santiago Aja-Fern´andez, Rodrigo de Luis Garc´ıa, Dacheng Tao, Xuelong Li (Ed.), Santiago Aja-Fern´andez, Rodrigo de Luis Garc´ıa, Dacheng Tao, Xuelong Li (Ed.), Tensors in Image Processing and Computer Vision: . Paper presented at Tensor in Image Processing and Computer Vision (pp. 407-428). Paper presented at Tensor in Image Processing and Computer Vision. Springer Science+Business Media B.V.
Open this publication in new window or tab >>Similar Tensor Arrays - A Framework for Storage of Tensor Array Data
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2009 (English)In: 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, p. 407-428Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2009 Edition: 1
Series
Advances in Pattern Recognition, ISSN 1617-7916
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-58091 (URN)10.1007/978-1-84882-299-3_19 (DOI)978-1-84882-298-6 (ISBN)978-1-84882-299-3 (ISBN)
Conference
Tensor in Image Processing and Computer Vision
Available from: 2010-07-29 Created: 2010-07-29 Last updated: 2018-01-12Bibliographically approved
Svensson, B. (2008). A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement. (Doctoral dissertation). : Institutionen för medicinsk teknik
Open this publication in new window or tab >>A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2008
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1171
Keywords
Medical image science, multidimensional filtering, image enhancement, image registration, image segmentation, filter networks, graphics processing units (GPU)
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-11271 (URN)978-91-7393-943-0 (ISBN)
Public defence
2008-04-18, Linden, Hus 421, Campus US, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2008-03-13 Created: 2008-03-13 Last updated: 2015-06-02
Svensson, B., Burdakov, O., Andersson, M. & Knutsson, H. (2008). Approximate Spectral Factorization for Design of Efficient Sub-Filter Sequences.
Open this publication in new window or tab >>Approximate Spectral Factorization for Design of Efficient Sub-Filter Sequences
2008 (English)Manuscript (preprint) (Other (popular science, discussion, etc.))
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13025 (URN)
Available from: 2008-03-13 Created: 2008-03-13 Last updated: 2015-06-02
Svensson, B., Andersson, M. & Knutsson, H. (2008). On Phase-Invariant Structure Tensors and Local Image Metrics. In: : . Paper presented at SSBA Symposium on Image Analysis, Lund Sweden.
Open this publication in new window or tab >>On Phase-Invariant Structure Tensors and Local Image Metrics
2008 (English)Conference paper, Published paper (Other academic)
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-61145 (URN)
Conference
SSBA Symposium on Image Analysis, Lund Sweden
Available from: 2010-11-04 Created: 2010-11-04 Last updated: 2013-08-28
Svensson, B., Burdakov, O., Andersson, M. & Knutsson, H. (2007). A New Approach For Treating Multiple Eextremal Points In Multi-Linear Least Squares Filter Design. In: Proceedings of the {SSBA} Symposium on Image Analysis, 2007: . Paper presented at Swedish Symposium in Image Analysis 2007 (SSBA), Linköping, Sweden,14-15 March 2007 (pp. 61-64).
Open this publication in new window or tab >>A New Approach For Treating Multiple Eextremal Points In Multi-Linear Least Squares Filter Design
2007 (English)In: Proceedings of the {SSBA} Symposium on Image Analysis, 2007, 2007, p. 61-64Conference paper, Published paper (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-38747 (URN)45477 (Local ID)45477 (Archive number)45477 (OAI)
Conference
Swedish Symposium in Image Analysis 2007 (SSBA), Linköping, Sweden,14-15 March 2007
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-06-02
Svensson, B., Brun, A., Andersson, M. & Knutsson, H. (2007). Estimation of Non-Cartesian Local Structure Tensor Fields. In: Bjarne Kjær Ersbøll, Kim Steenstrup Pedersen (Ed.), Bjarne Kjær Ersbøll, Kim Steenstrup Pedersen (Ed.), Image Analysis: 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007. Paper presented at 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007 (pp. 948-957). Springer Berlin/Heidelberg, 4522/2007
Open this publication in new window or tab >>Estimation of Non-Cartesian Local Structure Tensor Fields
2007 (English)In: 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, p. 948-957Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 4522
National Category
Signal Processing
Identifiers
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)
Conference
15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007
Available from: 2008-03-13 Created: 2008-03-13 Last updated: 2018-02-15Bibliographically approved
Brun, A., Svensson, B., Westin, C.-F., Herberthson, M., Wrangsjö, A. & Knutsson, H. (2007). Filtering Vector-Valued Images using Importance Sampling. In: Proceedings of the {SSBA} Symposium on Image Analysis,2007: . Paper presented at Symposium on Image Analysis {SSBA}, Linköping, Sweden March 14-15 2007.
Open this publication in new window or tab >>Filtering Vector-Valued Images using Importance Sampling
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2007 (English)In: Proceedings of the {SSBA} Symposium on Image Analysis,2007, 2007Conference paper, Published paper (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-38749 (URN)45479 (Local ID)45479 (Archive number)45479 (OAI)
Conference
Symposium on Image Analysis {SSBA}, Linköping, Sweden March 14-15 2007
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-08-28
Brun, A., Svensson, B., Westin, C.-F., Herberthson, M., Wrangsjö, A. & Knutsson, H. (2007). Using Importance Sampling for Bayesian Feature Space Filtering. In: Kjær Bjarne Ersbøll and Kim Steenstrup Pedersen (Ed.), Proceedings of the 15th Scandinavian conference on image analysis: . Paper presented at The 15th Scandinavian conference on image analysis, June 10-24, Aalborg, Denmark (pp. 818-827). Berlin, Heidelberg: Springer-Verlag
Open this publication in new window or tab >>Using Importance Sampling for Bayesian Feature Space Filtering
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2007 (English)In: Proceedings of the 15th Scandinavian conference on image analysis / [ed] Kjær Bjarne Ersbøll and Kim Steenstrup Pedersen, Berlin, Heidelberg: Springer-Verlag , 2007, p. 818-827Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer-Verlag, 2007
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Vol. 4522
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-38745 (URN)10.1007/978-3-540-73040-8_83 (DOI)000247364000083 ()45475 (Local ID)978-3-540-73039-2 (ISBN)45475 (Archive number)45475 (OAI)
Conference
The 15th Scandinavian conference on image analysis, June 10-24, Aalborg, Denmark
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-10-08Bibliographically approved
Svensson, B., Andersson, M., Smedby, Ö. & Knutsson, H. (2006). Efficient 3-D Adaptive Filtering for Medical Image Enhancement. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.: . Paper presented at 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 6-9 April 2006, Arlington, VA, USA (pp. 996-999). IEEE
Open this publication in new window or tab >>Efficient 3-D Adaptive Filtering for Medical Image Enhancement
2006 (English)In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., IEEE , 2006, p. 996-999Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2006
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13024 (URN)10.1109/ISBI.2006.1625088 (DOI)000244446000252 ()0-7803-9576-X (ISBN)
Conference
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 6-9 April 2006, Arlington, VA, USA
Available from: 2008-03-13 Created: 2008-03-13 Last updated: 2014-01-31Bibliographically approved
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