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Supporting Quantitative Visual Analysis in Medicine and Biology in the Presence of Data Uncertainty
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Scientific Visualization Group)
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The advents of technologies have led to tremendous increases in the diversity and size of the available data. In the field of medicine, the advancements in medical imaging technologies have dramatically improved the quality of the acquired data, such as a higher resolution and higher signal-to-noise ratio. In addition, the dramatic reduction of the acquisition time has enabled the studies of organs under function.At the same pace, the progresses in the field of biology and bioinformatics have led to stable automatic algorithms for the generation of biological data. As the amount of the available data and the complexity increase, there have been great demands on efficient analysis and visualization techniques to support quantitative visual analysis of the huge amount of data that we are facing.

This thesis aims at supporting quantitative visual analysis in the presence of data uncertainty within the context of medicine and biology. In this thesis, we present several novel analysis techniques and visual representations to achieve these goals. The results presented in this thesis cover a wide range of applications, which reflects the interdisciplinary nature of scientific visualization, as visualization is not for the sake of visualization. The advances in visualization enable the advances in other fields.

In typical clinical applications or research scenarios, it is common to have data from different modalities. By combining the information from these data sources, we can achieve better quantitative analysis as well as visualization. Nevertheless, there are many challenges involved along the process such as the co-registration, differences in resolution, and signal-to-noise ratio. We propose a novel approach that uses light as an information transporter to address the challenges involved when dealing with multimodal data.

When dealing with dynamic data, it is essential to identify features of interest across the time steps to support quantitative analyses. However, this is a time-consuming process and is prone to inconsistencies and errors. To address this issue, we propose a novel technique that enables an automatic tracking of identified features of interest across time steps in dynamic datasets.

Although technological advances improve the accuracy of the acquired data, there are other sources of uncertainty that need to be taken into account. In this thesis, we propose a novel approach to fuse the derived uncertainty from different sophisticated algorithms in order to achieve a new set of outputs with a lower level of uncertainty. In addition, we also propose a novel visual representation that not only supports comparative visualization, but also conveys the uncertainty in the parameters of a complex system.

Over past years, we have witnessed the rapid growth of available data in the field of biology. The sequence alignments of the top 20 protein domains and families have a large number of sequences, ranging from more than 70,000 to approximately 400,000 sequences. Consequently, it is difficult to convey features using the traditional representation. In this thesis, we propose a novel representation that facilitates the identification of gross trend patterns and variations in large-scale sequence alignment data.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. , 146 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1569
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-103799DOI: 10.3384/diss.diva-103799ISBN: 978-91-7519-415-8 (print)OAI: oai:DiVA.org:liu-103799DiVA: diva2:691439
Public defence
2014-03-07, Dome, Visualization Center, Kungsgatan 54, Norrköping, 10:00 (English)
Opponent
Supervisors
Note

The ISBN 978-91-7519-514-8 on the title page is incorrect. The correct ISBN is 978-91-7519-415-8.

Available from: 2014-01-28 Created: 2014-01-27 Last updated: 2015-09-22Bibliographically approved
List of papers
1. Analyzing and Reducing DTI Tracking Uncertainty by Combining Deterministic and Stochastic Approaches
Open this publication in new window or tab >>Analyzing and Reducing DTI Tracking Uncertainty by Combining Deterministic and Stochastic Approaches
2013 (English)In: Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I / [ed] Bebis, G.; Boyle, R.; Parvin, B.; Koracin, D.; Li, B.; Porikli, F.; Zordan, V.; Klosowski, J.; Coquillart, S.; Luo, X.; Chen, M.; Gotz, D., Springer Berlin/Heidelberg, 2013, 266-279 p.Conference paper, Published paper (Refereed)
Abstract [en]

Diffusion Tensor Imaging (DTI) in combination with fiber tracking algorithms enables visualization and characterization of white matter structures in the brain. However, the low spatial resolution associated with the inherently low signal-to-noise ratio of DTI has raised concerns regarding the reliability of the obtained fiber bundles. Therefore, recent advancements in fiber tracking algorithms address the accuracy of the reconstructed fibers. In this paper, we propose a novel approach for analyzing and reducing the uncertainty of densely sampled 3D DTI fibers in biological specimens. To achieve this goal, we derive the uncertainty in the reconstructed fiber tracts using different deterministic and stochastic fiber tracking algorithms. Through a unified representation of the derived uncertainty, we generate a new set of reconstructed fiber tracts that has a lower level of uncertainty. We will discuss our approach in detail and present the results we could achieve when applying it to several use cases.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; Vol. 8034
Keyword
Computer science, Computer graphics, Computer vision, Optical pattern recognition, Bioinformatics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-103795 (URN)10.1007/978-3-642-41914-0_27 (DOI)000335391300027 ()978-3-642-41938-6 (ISBN)978-3-642-41914-0 (ISBN)
Conference
ISVC 2013 : 9th International Symposium on Visual Computing, July 29-31, Rethymnon, Crete, Greece
Available from: 2014-01-27 Created: 2014-01-27 Last updated: 2017-03-17Bibliographically approved
2. Large-Scale Multiple Sequence Alignment Visualization through Gradient Vector Flow Analysis
Open this publication in new window or tab >>Large-Scale Multiple Sequence Alignment Visualization through Gradient Vector Flow Analysis
2013 (English)In: IEEE Symposium on Biological Data Visualization (BioVis), 2013 / [ed] Jos Roerdink and Jessie Kennedy, IEEE , 2013, 9-16 p.Conference paper, Published paper (Refereed)
Abstract [en]

Multiple sequence alignment (MSA) is essential as an initial step in studying molecular phylogeny as well as during the identification of genomic rearrangements. Recent advances in sequencing techniques have led to a tremendous increase in the number of sequences to be analyzed. As a result, a greater demand is being placed on visualization techniques, as they have the potential to reveal the underlying information in large-scale MSAs. In this work, we present a novel visualization technique for conveying the patterns in large-scale MSAs. By applying gradient vector flow analysis to the MSA data, we can extract and visually emphasize conservations and other patterns that are relevant during the MSA exploration process. In contrast to the traditional visual representation of MSAs, which exploits color-coded tables, the proposed visual metaphor allows us to provide an overview of large MSAs as well as to highlight global patterns, outliers, and data distributions. We will motivate and describe the proposed algorithm, and further demonstrate its application to large-scale MSAs.

Place, publisher, year, edition, pages
IEEE, 2013
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-103797 (URN)10.1109/BioVis.2013.6664341 (DOI)978-1-4799-1658-0 (ISBN)
Conference
IEEE Symposium on Biological Data Visualization, October 13-14, Atlanta, USA
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research Council, 2011-4113Swedish e‐Science Research Center
Available from: 2014-01-27 Created: 2014-01-27 Last updated: 2017-03-17
3. Deriving and Visualizing Uncertainty in Kinetic PET Modeling
Open this publication in new window or tab >>Deriving and Visualizing Uncertainty in Kinetic PET Modeling
2012 (English)In: Eurographics Workshop on Visual Computing for Biology and Medicine, 2012 / [ed] T. Ropinski, A. Ynnerman, C. Botha, and J. Roerdink, The Eurographics Association , 2012, 107-114 p.Conference paper, Published paper (Refereed)
Abstract [en]

Kinetic modeling is the tool of choice when developing new positron emission tomography (PET) tracers for quantitative functional analysis. Several approaches are widely used to facilitate this process. While all these approaches are inherently different, they are still subject to uncertainty arising from various stages of the modeling process. In this paper we propose a novel approach for deriving and visualizing uncertainty in kinetic PET modeling. We distinguish between intra- and inter-model uncertainties. While intra-model uncertainty allows us to derive uncertainty based on a single modeling approach, inter-model uncertainty arises from the differences of the results of different approaches. To derive intra-model uncertainty we exploit the covariance matrix analysis. The inter-model uncertainty is derived by comparing the outcome of three standard kinetic PET modeling approaches. We derive and visualize this uncertainty to exploit it as a basis for changing model input parameters with the ultimate goal to reduce the modeling uncertainty and thus obtain a more realistic model of the tracer under investigation. To support this uncertainty reduction process, we visually link abstract and spatial data by introducing a novel visualization approach based on the ThemeRiver metaphor, which has been modified to support the uncertainty-aware visualization of parameter changes between spatial locations. We have investigated the benefits of the presented concepts by conducting an evaluation with domain experts.

Place, publisher, year, edition, pages
The Eurographics Association, 2012
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778 (print) 2070-5786 (online) ; 2012
National Category
Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-92834 (URN)10.2312/VCBM/VCBM12/107-114 (DOI)978-3-905674-38-5 (ISBN)
Conference
The third Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM 2012), 27-28 September 2012, Norrköping, Sweden
Available from: 2013-05-24 Created: 2013-05-24 Last updated: 2017-03-17
4. Concurrent Volume Visualization of Real-Time fMRI
Open this publication in new window or tab >>Concurrent Volume Visualization of Real-Time fMRI
Show others...
2010 (English)In: Proceedings of the 8th IEEE/EG International Symposium on Volume Graphics / [ed] Ruediger Westermann and Gordon Kindlmann, Goslar, Germany: Eurographics - European Association for Computer Graphics, 2010, 53-60 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel approach to interactive and concurrent volume visualization of functional Magnetic Resonance Imaging (fMRI). While the patient is in the scanner, data is extracted in real-time using state-of-the-art signal processing techniques. The fMRI signal is treated as light emission when rendering a patient-specific high resolution reference MRI volume, obtained at the beginning of the experiment. As a result, the brain glows and emits light from active regions. The low resolution fMRI signal is thus effectively fused with the reference brain with the current transfer function settings yielding an effective focus and context visualization. The delay from a change in the fMRI signal to the visualization is approximately 2 seconds. The advantage of our method over standard 2D slice based methods is shown in a user study. We demonstrate our technique through experiments providing interactive visualization to the fMRI operator and also to the test subject in the scanner through a head mounted display.

Place, publisher, year, edition, pages
Goslar, Germany: Eurographics - European Association for Computer Graphics, 2010
Series
Eurographics/IEEE VGTC Symposium on Volume Graphics, ISSN 1727-8376 ; VG10
Keyword
fMRI, Direct volume rendering, Local ambient occlusion, Real-time, Biofeedback
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-58060 (URN)10.2312/VG/VG10/053-060 (DOI)978-3-905674-23-1 (ISBN)
Conference
8th IEEE/EG International Symposium on Volume Graphics, Norrköping, Sweden, 2-3 May, 2010
Projects
CADICS
Available from: 2010-07-27 Created: 2010-07-27 Last updated: 2015-11-04Bibliographically approved
5. Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform
Open this publication in new window or tab >>Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform
2013 (English)In: Proceedings of SIGRAD 2013, Visual Computing, June 13-14, 2013, Norrköping, Sweden / [ed] Jonas Unger and Timo Ropinski, Linköping: Linköping University Electronic Press, 2013, 11-16 p.Conference paper, Published paper (Refereed)
Abstract [en]

Recent advances in medical imaging technology enable dynamic acquisitions of objects under movement. The acquired dynamic data has shown to be useful in different application scenarios. However, the vast amount of time-varying data put a great demand on robust and efficient algorithms for extracting and interpreting the underlying information. In this paper, we present a gpu-based approach for feature tracking in time-varying volumetric data set based on the Scale Invariant Feature Transform (SIFT) algorithm. Besides, the improved performance, this enables us to robustly and efficiently track features of interest in the volumetric data over the time domain. As a result, the proposed approach can serve as a foundation for more advanced analysis on the features of interest in dynamic data sets. We demonstrate our approach using a time-varying data set for the analysis of internal motion of breathing lungs.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686 (print), 1650-3740 (online) ; 94
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-103798 (URN)978-91-7519-455-4 (ISBN)
Conference
SIGRAD Conference on Visual Computing, June 13-14, 2013, Norrköping, Sweden
Available from: 2014-01-27 Created: 2014-01-27 Last updated: 2017-03-17Bibliographically approved
6. Quantitative Analysis of Knee Movement Patterns through Comparative Visualization
Open this publication in new window or tab >>Quantitative Analysis of Knee Movement Patterns through Comparative Visualization
2014 (English)Manuscript (preprint) (Other academic)
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-103926 (URN)
Conference
ISVC 2013 : 9th International Symposium on Visual Computing, July 29-31, Rethymnon, Crete, Greece
Available from: 2014-02-03 Created: 2014-02-03 Last updated: 2017-03-17Bibliographically approved

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  • nn-NO
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