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Multi-Dimensional Transfer Function Design Using Sorted Histograms
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.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.ORCID iD: 0000-0002-9466-9826
2006 (English)In: Proceedings Eurographics/IEEE International Workshop on Volume Graphics 2006, Boston, USA, 2006, 1-8 p.Conference paper, Published paper (Other academic)
Abstract [en]

Multi-dimensional Transfer Functions (MDTFs) are increasingly used in volume rendering to produce high quality visualizations of complex data sets. A major factor limiting the use of MDTFs is that the available design tools have not been simple enough to reach wide usage outside of the research context, for instance in clinical medical imaging. In this paper we address this problem by defining an MDTF design concept based on improved histogram display and interaction in an exploratory process. To this end we propose sorted histograms, 2D histograms that retain the intuitive appearance of a traditional 1D histogram while conveying a second attribute. We deploy the histograms in medical visualizations using data attributes capturing domain knowledge e.g. in terms of homogeneity and typical surrounding of tissues. The resulting renderings demonstrate that the proposed concept supports a vast number of visualization possibilities based on multi-dimensional attribute data.

Place, publisher, year, edition, pages
2006. 1-8 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13948DOI: 10.2312/VG/VG06/001-008OAI: oai:DiVA.org:liu-13948DiVA: diva2:22278
Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
In thesis
1. Efficient Methods for Direct Volume Rendering of Large Data Sets
Open this publication in new window or tab >>Efficient Methods for Direct Volume Rendering of Large Data Sets
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Direct Volume Rendering (DVR) is a technique for creating images directly from a representation of a function defined over a three-dimensional domain. The technique has many application fields, such as scientific visualization and medical imaging. A striking property of the data sets produced within these fields is their ever increasing size and complexity. Despite the advancements of computing resources these data sets seem to grow at even faster rates causing severe bottlenecks in terms of data transfer bandwidths, memory capacity and processing requirements in the rendering pipeline.

This thesis focuses on efficient methods for DVR of large data sets. At the core of the work lies a level-of-detail scheme that reduces the amount of data to process and handle, while optimizing the level-of-detail selection so that high visual quality is maintained. A set of techniques for domain knowledge encoding which significantly improves assessment and prediction of visual significance for blocks in a volume are introduced. A complete pipeline for DVR is presented that uses the data reduction achieved by the level-of-detail selection to minimize the data requirements in all stages. This leads to reduction of disk I/O as well as host and graphics memory. The data reduction is also exploited to improve the rendering performance in graphics hardware, employing adaptive sampling both within the volume and within the rendered image.

The developed techniques have been applied in particular to medical visualization of large data sets on commodity desktop computers using consumer graphics processors. The specific application of virtual autopsies has received much interest, and several developed data classification schemes and rendering techniques have been motivated by this application. The results are, however, general and applicable in many fields and significant performance and quality improvements over previous techniques are shown.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap, 2006
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1043
Keyword
Computer Graphics, Scientific Visualization, Medical Imaging, Volume Rendering, Raycasting, Transfer Functions, Level-of-detail, Fuzzy Classification, Virtual Autopsies
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-7232 (URN)91-85523-05-4 (ISBN)
Public defence
2006-10-06, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 09:15 (English)
Opponent
Supervisors
Note
On the defence date the status of article IX was Accepted.Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
2. 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, Anders

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