Efficient Medical Volume Visualization: An Approach Based on Domain Knowledge
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1125
Scientific Visualization, Medical Imaging, Computer Graphics, Volume Rendering, Transfer Function, Level-of-detail, Fuzzy Classification, Uncertainty Visualization, Virtual Autopsies
Medical Laboratory and Measurements Technologies
IdentifiersURN: urn:nbn:se:liu:diva-9561ISBN: 978-91-85831-10-4OAI: oai:DiVA.org:liu-9561DiVA: diva2:23985
2007-09-14, Berzeliussalen, Hälsouniversitetet, Linköping, 13:15 (English)
Bartz, Dirk, Prof. Dr.
Ynnerman, AndersKnutsson, Hans
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