Visualization of uncertain multivariate 3d scalar fields

This dissertation presents a visualization system that enables the exploration of relationships in multivariate scalar volume data with statistically uncertain values. The n-Dimensional Volume Explorer (nDive) creates multiple visualizations that emphasize different features of the data. nDive provides data selection mechanisms that are linked between views to enable the comparison of separate data populations in the presence of uncertainty.Incorporating representations of uncertainty into data visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. The challenge of visualizing uncertain data increases with the complexity of the data set. nDive separates the visualization into multiple views: a glyph-based spatial visualization and abstract multivariate density plots that incorporate uncertainty. The spatial visualization, Scaled Data-Driven Spheres (SDDS), distributes (for each variable) a set of scaled, colored spheres in the sample space. A user study demonstrates that viewers are faster and more accurate using SDDS to identify spatial values and relationships as compared to superquadric glyphs, an alternative technique. The abstract plots complement SDDS by preattentively focusing the viewer on trustworthy data points using the probability density function of the data. These views, coupled with novel interaction techniques that utilize data value uncertainty, enable the identification of relationships while avoiding false conclusions based on uncertain data. The primary application of nDive is aiding radiologists who use magnetic resonance spectroscopy (MRS) to better understand the composition of abnormalities such as brain tumors and improve diagnostic accuracy. This work demonstrates how nDive has been successfully used to identify metabolic signatures for multiple types of tumors.