Uncertainty-Aware Guided Volume Segmentation

—Although direct volume rendering is established as a powerful tool for the visualization of volumetric data, efficient and reliable feature detection is still an open topic. Usually, a tradeoff between fast but imprecise classification schemes and accurate but time-consuming segmentation techniques has to be made. Furthermore, the issue of uncertainty introduced with the feature detection process is completely neglected by the majority of existing approaches. In this paper we propose a guided probabilistic volume segmentation approach that focuses on the minimization of uncertainty. In an iterative process, our system continuously assesses uncertainty of a random walker-based segmentation in order to detect regions with high ambiguity, to which the user’s attention is directed to support the correction of potential misclassifications. This reduces the risk of critical segmentation errors and ensures that information about the segmentation’s reliability is conveyed to the user...

• Type: Journal
• Year: 2010
• Where: TVCG
• Authors: Jörg-Stefan Praßni, Timo Ropinski, Klaus Hinrichs
• Keywords: Feature Detection, Probabilistic Volume Segmentation, Time-consuming Segmentation Techniques, TVCG 2010