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
Regularization in Medical Image Registration using Global Linear Optimization
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
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
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Common problems in image registration include having large parts of the images contain noisy, uncertain, missing or impossible motion. Regularization is the field that aims to overcome these problems. In this article, we propose a novel framework : Global Linear Optimization (GLO) which we demonstrate has the capabilities to simultaneously and globally regularize with respect to : (1) anisotropic certainty of prior motion field, (2) sliding of organ boundaries and (3) incompressibility of organ interiors. The power of the presented framework consists of being able to spatially adapt which subsets of the data each constraint should affect and then solve a large sparse linear equations system which automatically propagates a solution over the data set through an overlapping localized metric. We demonstrate the validity of the methods and the power of the GLO framework on relevant test cases and on medical data from the DIR-lab.

Keyword [en]
Keywords—Image Registration, Medical Image Analysis, Regularization, Adaptive Filtering, Medical Atlases, Global Methods, Optimization, Global Linear Optimization, Structure Tensor, Anisotropic Filtering, Partial Differential Equations
National Category
Medical Image Processing Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-117140OAI: oai:DiVA.org:liu-117140DiVA: diva2:805940
Available from: 2015-04-17 Created: 2015-04-17 Last updated: 2015-04-17Bibliographically approved
In thesis
1. A Global Linear Optimization Framework for Adaptive Filtering and Image Registration
Open this publication in new window or tab >>A Global Linear Optimization Framework for Adaptive Filtering and Image Registration
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Digital medical atlases can contain anatomical information which is valuable for medical doctors in diagnosing and treating illnesses. The increased availability of such atlases has created an interest for computer algorithms which are capable of integrating such atlas information into patient specific dataprocessing. The field of medical image registration aim at calculating how to match one medical image to another. Here the atlas information could give important hints of which kinds of motion are plausible in different locations of the anatomy. Being able to incorporate such atlas specific information could potentially improve the matching of images and plausibility of image registration - ultimately providing a more correct information on which to base health care diagnosis and treatment decisions.

In this licentiate thesis a generic signal processing framework is derived : Global Linear Optimization (GLO). The power of the GLO framework is first demonstrated quantitatively in a very high performing image denoiser. Important proofs of concepts are then made deriving and implementing three important capabilities regarding adaptive filtering of vector fields in medica limage registration:

  1. Global regularization with local anisotropic certainty metric.
  2. Allowing sliding motion along organ and tissue boundaries.
  3. Enforcing an incompressible motion in specific areas or volumes.

In the three publications included in this thesis, the GLO framework is shown to be able to incorporate one each of these capabilities. In the third and final paper a demonstration is made how to integrate more and more of the capabilities above into the same GLO to perform adaptive processing on relevant clinical data. It is shown how each added capability improves the result of the image registration. In the end of the thesis there is a discussion which highlights the advantage of the contributions made as compared to previous methods in the scientific literature.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 61 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1711
Keyword
Global Linear Optimization, Linear Optimization, Regularization, Medical Image Registration, Structure Tensor
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-117024 (URN)10.3384/diss.diva-117024 (DOI)978-91-7519-108-9 (ISBN)
Presentation
2015-05-07, IMT1, plan 13, Campus US, Linköpings universitet, Linköping, 15:15 (English)
Opponent
Supervisors
Projects
Dynamic Context Atlases for Image Denoising and Patient Safety
Funder
Linnaeus research environment CADICSSwedish Research Council, 2011-5176
Available from: 2015-04-17 Created: 2015-04-10 Last updated: 2015-04-20Bibliographically approved

Open Access in DiVA

No full text

Authority records BETA

Johansson, GustafAndersson, MatsKnutsson, Hans

Search in DiVA

By author/editor
Johansson, GustafAndersson, MatsKnutsson, Hans
By organisation
Medical InformaticsThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)
Medical Image ProcessingOther Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 204 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