Multichannel transfer function with dimensionality reduction

Handle URI:
http://hdl.handle.net/10754/598904
Title:
Multichannel transfer function with dimensionality reduction
Authors:
Kim, Han Suk; Schulze, Jürgen P.; Cone, Angela C.; Sosinsky, Gina E.; Martone, Maryann E.
Abstract:
The design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data.
Citation:
Kim HS, Schulze JP, Cone AC, Sosinsky GE, Martone ME (2010) Multichannel transfer function with dimensionality reduction. Visualization and Data Analysis 2010. Available: http://dx.doi.org/10.1117/12.839526.
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
Visualization and Data Analysis 2010
KAUST Grant Number:
US 2008-107
Issue Date:
17-Jan-2010
DOI:
10.1117/12.839526
PubMed ID:
20582228
PubMed Central ID:
PMC2891081
Type:
Conference Paper
Sponsors:
This publication was made possible by Grant Number (NCRR P41-RR004050) from the National Center forResearch Resources (NCRR), a part of the National Institutes of Health (NIH). Its contents are solely the responsibilityof the authors and do not necessarily represent the official views of the NIH. This publication is basedin part on work supported by Award No. US 2008-107, made by King Abdullah University of Science and Technology(KAUST), by NIH Award (NIGMS F32GM092457) and by National Science Foundation Awards (NSFMCB-0543934 and OCE-0835839). Finally, the authors would like to thank Lawrence Saul and the anonymousreviewers for their helpful comments.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorKim, Han Suken
dc.contributor.authorSchulze, Jürgen P.en
dc.contributor.authorCone, Angela C.en
dc.contributor.authorSosinsky, Gina E.en
dc.contributor.authorMartone, Maryann E.en
dc.date.accessioned2016-02-25T13:43:26Zen
dc.date.available2016-02-25T13:43:26Zen
dc.date.issued2010-01-17en
dc.identifier.citationKim HS, Schulze JP, Cone AC, Sosinsky GE, Martone ME (2010) Multichannel transfer function with dimensionality reduction. Visualization and Data Analysis 2010. Available: http://dx.doi.org/10.1117/12.839526.en
dc.identifier.pmid20582228en
dc.identifier.doi10.1117/12.839526en
dc.identifier.urihttp://hdl.handle.net/10754/598904en
dc.description.abstractThe design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data.en
dc.description.sponsorshipThis publication was made possible by Grant Number (NCRR P41-RR004050) from the National Center forResearch Resources (NCRR), a part of the National Institutes of Health (NIH). Its contents are solely the responsibilityof the authors and do not necessarily represent the official views of the NIH. This publication is basedin part on work supported by Award No. US 2008-107, made by King Abdullah University of Science and Technology(KAUST), by NIH Award (NIGMS F32GM092457) and by National Science Foundation Awards (NSFMCB-0543934 and OCE-0835839). Finally, the authors would like to thank Lawrence Saul and the anonymousreviewers for their helpful comments.en
dc.publisherSPIE-Intl Soc Optical Engen
dc.subjectDimensionality reductionen
dc.subjectLight microscopy imagingen
dc.subjectMulti-channel volumeen
dc.subjectTransfer functionen
dc.subjectVolume renderingen
dc.titleMultichannel transfer function with dimensionality reductionen
dc.typeConference Paperen
dc.identifier.journalVisualization and Data Analysis 2010en
dc.identifier.pmcidPMC2891081en
dc.contributor.institutionDepartment of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA.en
kaust.grant.numberUS 2008-107en

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