liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Efficient 3-D Adaptive Filtering for Medical Image Enhancement
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). 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.ORCID iD: 0000-0002-7750-1917
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2006 (English)In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., IEEE , 2006, 996-999 p.Conference 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. 996-999 p.
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13024DOI: 10.1109/ISBI.2006.1625088ISI: 000244446000252ISBN: 0-7803-9576-X (print)OAI: oai:DiVA.org:liu-13024DiVA: diva2:17685
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
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

Open Access in DiVA

No full text

Other links

Publisher's full textLink to Ph.D. thesis

Authority records BETA

Svensson, BjörnAndersson, MatsSmedby, ÖrjanKnutsson, Hans

Search in DiVA

By author/editor
Svensson, BjörnAndersson, MatsSmedby, ÖrjanKnutsson, Hans
By organisation
Medical InformaticsCenter for Medical Image Science and Visualization (CMIV)The Institute of TechnologyMedical RadiologyFaculty of Health SciencesDepartment of Radiology in Linköping
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 840 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf