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Transfer Function Design Toolbox for Full-Color Volume Datasets
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Scientific Visualization Group)ORCID iD: 0000-0003-1511-5006
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Scientific Visualization Group)ORCID iD: 0000-0001-7285-0483
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9288-5322
Linköping University, Center for Medical Image Science and Visualization (CMIV). Leeds Teaching Hospitals NHS Trust, United Kingdom.
Show others and affiliations
2017 (English)In: 2017 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS), IEEE, IEEE, 2017, p. 171-179Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.

Place, publisher, year, edition, pages
IEEE, 2017. p. 171-179
Series
IEEE Pacific Visualization Symposium, ISSN 2165-8765
Keywords [en]
Transfer Function, full color data, volume rendering, visualization
National Category
Medical Image Processing Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-134851DOI: 10.1109/PACIFICVIS.2017.8031591ISI: 000425212300020ISBN: 978-1-5090-5738-2 (electronic)OAI: oai:DiVA.org:liu-134851DiVA, id: diva2:1077300
Conference
IEEE Pacific Visualization Symposium (PacificVis 2017)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish e‐Science Research Center
Note

Funding agencies: Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Swedish e-Science Research Centre (SeRC); DigiPat project by VINNOVA [2014-04257]

Available from: 2017-02-27 Created: 2017-02-27 Last updated: 2018-03-21

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Falk, MartinHotz, IngridLjung, PatricYnnerman, AndersLundström, Claes
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Media and Information TechnologyFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)
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