The principal goal of visualization is to create a visual representation of complex information and large datasets in order to gain insight and understanding. Our current research focuses on methods for handling uncertainty stemming from data acquisition and algorithmic sources. Most visualization methods, especially those applied to 3D data, implicitly use some form of classification or segmentation to eliminate unimportant regions and illuminate those of interest. The process of classification is inherently uncertain; in many cases the source data contains error and noise, data transformations such as filtering can further introduce and magnify the uncertainty. More advanced classification methods rely on some sort of model or statistical method to determine what is and is not a feature of interest. While these classification methods can model uncertainty or fuzzy probabilistic memberships, they typically only provide discrete, maximum a-posteriori memberships. It is vital that visualization methods provide the user access to uncertainly in classification or image generation if the results of the visualization are to be trusted.
@InProceedings{ kniss:2008:MUMD,
author = {Joe Michael Kniss},
title = {Managing Uncertainty in Visualization and Analysis
of Medical Data},
booktitle = {{IEEE} 5th International Symposium on Biomedical
Imaging: From Nano to Macro, 2008. (ISBI 2008)},
pages = {832--835},
year = {2008},
}