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Estimation of Non-Cartesian Local Structure Tensor Fields
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
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, 948-957 p.Conference 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. Vol. 4522/2007, 948-957 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 4522
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-13023DOI: 10.1007/978-3-540-73040-8_96ISI: 000247364000096ISBN: 978-3-540-73039-2 (print)ISBN: 978-3-540-73040-8 (print)OAI: oai:DiVA.org:liu-13023DiVA: diva2:17684
Conference
15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007
Available from: 2008-03-13 Created: 2008-03-13 Last updated: 2015-10-08Bibliographically approved
In thesis
1. A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement
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
Keyword
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

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Svensson, BjörnBrun, AndersAndersson, MatsKnutsson, Hans

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