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Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9368-0177
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9288-5322
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9466-9826
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9446-6981
2007 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 13, no 6, 1648-1655 p.Article in journal (Refereed) Published
Abstract [en]

Direct volume rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.

Place, publisher, year, edition, pages
2007. Vol. 13, no 6, 1648-1655 p.
Keyword [en]
uncertainty, medical visualization, probability, transfer function, volume rendering
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-14597DOI: 10.1109/TVCG.2007.70518ISI: 000250401100076OAI: oai:DiVA.org:liu-14597DiVA: diva2:23984
Available from: 2007-08-24 Created: 2007-08-24 Last updated: 2017-12-13
In thesis
1. Efficient Medical Volume Visualization: An Approach Based on Domain Knowledge
Open this publication in new window or tab >>Efficient Medical Volume Visualization: An Approach Based on Domain Knowledge
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Direct Volume Rendering (DVR) is a visualization technique that has proved to be a very powerful tool in many scientific visualization applications. Diagnostic medical imaging is one domain where DVR could provide clear benefits in terms of unprecedented possibilities for analysis of complex cases and highly efficient work flow for certain routine examinations. The full potential of DVR in the clinical environment has not been reached, however, primarily due to limitations in conventional DVR methods and tools.

This thesis presents methods addressing four major challenges for DVR in clinical use. The foundation of all methods is to incorporate the domain knowledge of the medical professional in the technical solutions. The first challenge is the very large data sets routinely produced in medical imaging today. To this end a multiresolution DVR pipeline is proposed, which dynamically prioritizes data according to the actual impact in the rendered image to be reviewed. Using this prioritization the system can reduce the data requirements throughout the pipeline and provide high performance and visual quality in any environment.

Another problem addressed is how to achieve simple yet powerful interactive tissue classification in DVR. The methods presented define additional attributes that effectively captures readily available medical knowledge. The task of tissue detection is also important to solve in order to improve efficiency and consistency of diagnostic image review. Histogram-based techniques that exploit spatial relations in the data to achieve accurate and robust tissue detection are presented in this thesis.

The final challenge is uncertainty visualization, which is very pertinent in clinical work for patient safety reasons. An animation method has been developed that automatically conveys feasible alternative renderings. The basis of this method is a probabilistic interpretation of the visualization parameters.

Several clinically relevant evaluations of the developed techniques have been performed demonstrating their usefulness. Although there is a clear focus on DVR and medical imaging, most of the methods provide similar benefits also for other visualization techniques and application domains.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap, 2007. 55 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1125
Keyword
Scientific Visualization, Medical Imaging, Computer Graphics, Volume Rendering, Transfer Function, Level-of-detail, Fuzzy Classification, Uncertainty Visualization, Virtual Autopsies
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-9561 (URN)978-91-85831-10-4 (ISBN)
Public defence
2007-09-14, Berzeliussalen, Hälsouniversitetet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2007-08-24 Created: 2007-08-24 Last updated: 2015-09-22

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Lundström, ClaesLjung, PatricYnnerman, AndersPersson, Anders

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Lundström, ClaesLjung, PatricYnnerman, AndersPersson, Anders
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Visual Information Technology and Applications (VITA)The Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)RadiologyFaculty of Health SciencesDepartment of Radiology in Linköping
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IEEE Transactions on Visualization and Computer Graphics
Medical Laboratory and Measurements Technologies

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