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.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): US 2008-107
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.