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Center for Nonlinear Studies |
Data visualization has become indispensable for efficient interpretation of large-scale data generated across diverse scientific domains, such as biomedical imaging and climate studies. Many critical decisions directly rely on the quality of data visualizations. Inaccuracies in visualizations cannot be averted due to uncertainties inherent in underlying data and non-linear transformations of data caused by the stages of visualization pipeline. The uncertainty in the final visualizations can adversely impact the decision-making process. The accurate quantification of uncertainties in data visualizations has, therefore, been recognized as the top research challenge for minimizing risks associated with scientific decisions. In our work, we statistically quantify positional variations in features of uncertain data for two applications. First, we study the interaction of the marching cubes algorithm with uncertain data for probabilistic quantification of positional variations in level-set extractions. Second, we study spatial variability in objects of known geometry arising from their finite-resolution imaging. Specifically, we perform our second study on electrodes of fixed geometry used for deep brain stimulation (DBS) surgery. Our uncertainty visualizations for level-set extraction and DBS electrode localization confirm the significance of incorporating statistical error analysis into computational models for visualization applications.
For questions, or to schedule time with Tushar, please contact: Chris Biwer, 505-665-8009, cmbiwer@lanl.gov
Host: Chris Biwer