Quantitatively Visualizing Uncertainty Information using Volume Ray-Casting Rendering, Linked View and Scatter Plot for Volumetric Data

Ji Ma, David Murphy, Cian O'Mathuna, Michael Hayes, and Gregory Provan


Uncertainty Visualization, Scientific Visualization, Information Visualization, Quantitative Visualization, Volumetric Data


Uncertainty visualization has been identified as one of the top research problems in visualization community [1-3, 13] and has received increasing attention over the past years. Various uncertainty visualization techniques have been proposed to depict the uncertainty information in different application domains. However, these traditional methods of uncertainty visualization often heavily rely on the perception of human visual system to recognize the uncertainty information and therefore are incapable to describe them quantitatively. For many visual analysis and exploration tasks it is always useful to make use of the quantitative visualization techniques from the information visualization community to assist users describing the data and understanding the phenomenon behind the data. Therefore in this paper, we explore and present a new method which combines techniques from both scientific visualization e.g., direct volume rendering (DVR) and information visualization e.g., linked view and scatter plot to quantitatively depict the uncertainty information. Moreover, we applied our technique to quantify and visualize the errors existed in multi-resolution (MR) data set from medical domain as an example to demonstrate its usability and effectiveness. The results from the experiment have proved that our method is promising.

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