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Proceedings Paper

Multichannel transfer function with dimensionality reduction
Author(s): Han Suk Kim; Jürgen P. Schulze; Angela C. Cone; Gina E. Sosinsky; Maryann E. Martone
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Paper Abstract

The design of transfer functions for volume rendering is a difficult task. This is particularly true for multichannel 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.

Paper Details

Date Published: 18 January 2010
PDF: 12 pages
Proc. SPIE 7530, Visualization and Data Analysis 2010, 75300A (18 January 2010); doi: 10.1117/12.839526
Show Author Affiliations
Han Suk Kim, Univ. of California, San Diego (United States)
Jürgen P. Schulze, Univ. of California, San Diego (United States)
Angela C. Cone, Univ. of California, San Diego (United States)
Gina E. Sosinsky, Univ. of California, San Diego (United States)
Maryann E. Martone, Univ. of California, San Diego (United States)

Published in SPIE Proceedings Vol. 7530:
Visualization and Data Analysis 2010
Jinah Park; Ming C. Hao; Pak Chung Wong; Chaomei Chen, Editor(s)

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