An Evaluation of Peak Finding for DVR Classification of Biological Data

Handle URI:
http://hdl.handle.net/10754/597524
Title:
An Evaluation of Peak Finding for DVR Classification of Biological Data
Authors:
Knoll, Aaron; Westerteiger, Rolf; Hagen, Hans
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.
Citation:
Knoll A, Westerteiger R, Hagen H (2012) An Evaluation of Peak Finding for DVR Classification of Biological Data. Visualization in Medicine and Life Sciences II: 91–106. Available: http://dx.doi.org/10.1007/978-3-642-21608-4_6.
Publisher:
Springer Science + Business Media
Journal:
Visualization in Medicine and Life Sciences II
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2012
DOI:
10.1007/978-3-642-21608-4_6
Type:
Book Chapter
ISSN:
1612-3786
Sponsors:
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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorKnoll, Aaronen
dc.contributor.authorWesterteiger, Rolfen
dc.contributor.authorHagen, Hansen
dc.date.accessioned2016-02-25T12:41:25Zen
dc.date.available2016-02-25T12:41:25Zen
dc.date.issued2012en
dc.identifier.citationKnoll A, Westerteiger R, Hagen H (2012) An Evaluation of Peak Finding for DVR Classification of Biological Data. Visualization in Medicine and Life Sciences II: 91–106. Available: http://dx.doi.org/10.1007/978-3-642-21608-4_6.en
dc.identifier.issn1612-3786en
dc.identifier.doi10.1007/978-3-642-21608-4_6en
dc.identifier.urihttp://hdl.handle.net/10754/597524en
dc.description.abstractIn 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.en
dc.description.sponsorshipThis 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.en
dc.publisherSpringer Science + Business Mediaen
dc.titleAn Evaluation of Peak Finding for DVR Classification of Biological Dataen
dc.typeBook Chapteren
dc.identifier.journalVisualization in Medicine and Life Sciences IIen
dc.contributor.institutionUniversity of Kaiserslautern, Kaiserslautern, Germanyen
kaust.grant.numberKUS-C1-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.