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Motion Field Regularization for Sliding Objects using Global Linear Optimization
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. (Medicinsk Bildanalys, Medical Image Analysis)
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. (Medicinsk Bildanalys, Medical Image Analysis)
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. (Medicinsk Bildanalys, Medical Image Analysis)ORCID iD: 0000-0002-9091-4724
2015 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

In image registration it is often necessary to employ  regularization in one form or another to be able to find a plausible  displacement field. In medical applications, it is useful to define  different constraints for different areas of the data. For instance  to measure if organs have moved as expected after a finished  treatment. One common problem is how to find plausible motion  vectors far away from known motion. This paper introduces a new  method to build and solve a Global Linear Optimizations (GLO)  problem with a novel set of terms which enable specification of  border areas to allow a sliding motion. The GLO approach is  important especially because it allows simultaneous incorporation of  several different constraints using information from medical atlases  such as localization and properties of organs. The power and  validity of the method is demonstrated using two simple, but  relevant 2D test images. Conceptual comparisons with previous  methods are also made to highlight the contributions made in this  paper. The discussion explains important future work and experiments  as well as exciting future improvements to the GLO framework.

Place, publisher, year, edition, pages
2015.
Keyword [en]
Image Registration, Missing Data, Medical Image Processing, Global Linear Optimization
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-112210OAI: oai:DiVA.org:liu-112210DiVA: diva2:764273
Conference
The 4th International Conference on Pattern Recognition Applications and Methods, Januari 10-12, Lisbon, Portugal
Projects
Dynamic Context Atlases for Image Denoising and Patient SafetyGlobal Linear Optimization
Funder
Swedish Research Council, 2011-5176Linnaeus research environment CADICS
Available from: 2014-11-18 Created: 2014-11-18 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

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SlidingObjectsGLO.pdf(398 kB)72 downloads
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Johansson, GustafAndersson, MatsKnutsson, Hans

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