Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
SIAM Journal on Scientific Computing
A Next Step: Visualizing Errors and Uncertainty
IEEE Computer Graphics and Applications
Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Visualization in Medicine: Theory, Algorithms, and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Modified fuzzy c-mean in medical image segmentation
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
IEEE Transactions on Visualization and Computer Graphics
Parameter Sensitivity Visualization for DTI Fiber Tracking
IEEE Transactions on Visualization and Computer Graphics
Uncertainty-Aware Guided Volume Segmentation
IEEE Transactions on Visualization and Computer Graphics
An Information-theoretic Framework for Visualization
IEEE Transactions on Visualization and Computer Graphics
A survey of medical image registration on graphics hardware
Computer Methods and Programs in Biomedicine
IEEE Transactions on Visualization and Computer Graphics
GPU-based Real-Time Approximation of the Ablation Zone for Radiofrequency Ablation
IEEE Transactions on Visualization and Computer Graphics
Visual 4D MRI blood flow analysis with line predicates
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
Uncertainty visualization in HARDI based on ensembles of ODFs
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
Conceptualizing Visual Uncertainty in Parallel Coordinates
Computer Graphics Forum
Volume rendering data with uncertainty information
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
Visualizing summary statistics and uncertainty
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Probexplorer: uncertainty-guided exploration and editing of probabilistic medical image segmentation
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
A user study of visualization effectiveness using EEG and cognitive load
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualizing the positional and geometrical variability of isosurfaces in uncertain scalar fields
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualization of Uncertainty without a Mean
IEEE Computer Graphics and Applications
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The medical visualization pipeline ranges from medical imaging processes over several data processing steps to the final rendering output. Each of these steps induces a certain amount of uncertainty based on errors or assumptions. The rendered images typically omit this information and allude to the fact that the shown information is the only possible truth. Medical doctors may base their diagnoses and treatments on these visual representations. However, many decisions made in the visualization pipeline are sensitive to small changes. To allow for a proper assessment of the data by the medical experts, the uncertainty that is inherent to the displayed information needs to be revealed. This is the task of uncertainty visualization. Recently, many approaches have been presented to tackle uncertainty visualization including a few techniques in the context of medical visualization, but they typically address one specific problem. At the moment, we lack a comprehensive understanding of what types of uncertainty exist in medical visualization and what their characteristics in terms of mathematical models are. In this paper, we work towards a taxonomy of uncertainty types in medical visualization. We categorize the types in an abstract form, describe them mathematically in a rigorous way, and discuss the visualization challenges of each type and the effectiveness of the existing techniques. Such a theoretical investigation allows for a better understanding of the visualization problems at hand, enables visualization researchers to relate other medical uncertainty visualization tasks to the taxonomy, and provides the foundation for novel, targeted visualization algorithms.