Abstract
In medicine and the life sciences, volume data are frequently entropic, containing numerous features at different scales as well as significant noise from the scan source. Conventional transfer function approaches for direct volume rendering have difficulty handling such data, resulting in poor classification or undersampled rendering. Peak finding addresses issues in classifying noisy data by explicitly solving for isosurfaces at desired peaks in a transfer function. As a result, one can achieve better classification and visualization with fewer samples and correspondingly higher performance. This paper applies peak finding to several medical and biological data sets, particularly examining its potential in directly rendering unfiltered and unsegmented data.
| Original language | English (US) |
|---|---|
| Title of host publication | Visualization in Medicine and Life Sciences II |
| Publisher | Springer Nature |
| Pages | 91-106 |
| Number of pages | 16 |
| ISBN (Print) | 9783642216077 |
| DOIs | |
| State | Published - 2012 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This work was supported by the German Research Foundation (DFG)through the University of Kaiserslautern International Research Training Group (IRTG 1131);as well as the National Science Foundation under grants CNS-0615194, CNS-0551724, CCF-0541113, IIS-0513212, and DOE VACET SciDAC, KAUST GRP KUS-C1-016-04. Additional thanks to Liz Jurrus and Tolga Tasdizen for the zebrafish data, to Rolf Westerteiger, Mathias Schottand Chuck Hansen for their assistance, and to the anonymous reviewers for their comments.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.