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Filter Networks for Efficient Estimation of Local 3-D Structure
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
2005 (English)In: IEEE International Conference on Image Processing (ICIP). 11-14 Sept, Genoa, Italy, 2005, Vol. 3, 573-576 p.Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
2005. Vol. 3, 573-576 p.
Keyword [en]
filter design, filter network, sparse filters, efficient filtering, local structure
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13026DOI: 10.1109/ICIP.2005.1530456OAI: oai:DiVA.org:liu-13026DiVA: diva2:17687
Available from: 2008-03-13 Created: 2008-03-13 Last updated: 2013-08-28
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, BjörnAndersson, MatsKnutsson, Hans

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