Informationbased Transfer Functions for Multimodal Visualization
Martin Haidacher, Stefan Bruckner, Armin Kanitsar, Meister
Eduard Gröller
CONFERENCE PAPER:
In Proceedings of VCBM 2008, pp. 101–108, 2008.
Abstract
Transfer functions are an essential part of volume visualization.
In multimodal visualization at least two values exist at every sample
point. Additionally, other parameters, such as gradient magnitude,
are often retrieved for each sample point. To find a good transfer
function for this high number of parameters is challenging because
of the complexity of this task. In this paper we present a general
informationbased approach for transfer function design in multimodal
visualization which is independent of the used modality types. Based
on information theory, the complex multidimensional transfer function
space is fused to allow utilization of a wellknown 2D transfer function
with a single value and gradient magnitude as parameters. Additionally,
a quantity is introduced which enables better separation of regions
with complementary information. The benefit of the new method in
contrast to other techniques is a transfer function space which is
easy to understand and which provides a better separation of different
tissues. The usability of the new approach is shown on examples of
different modalities.
Published
Proceedings of VCBM 2008
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BibTeX
@INPROCEEDINGS{Haidacher2008ITF,
author = {Martin Haidacher and Stefan Bruckner and Armin Kanitsar and Meister
Eduard Gr{\"o}ller},
title = {Informationbased Transfer Functions for Multimodal Visualization},
booktitle = {Proceedings of VCBM 2008},
year = {2008},
editor = {C.P Botha, G. Kindlmann, W.J. Niessen, and B. Preim},
pages = {101108},
month = oct,
publisher = {Eurographics Association},
abstract = {Transfer functions are an essential part of volume visualization.
In multimodal visualization at least two values exist at every sample
point. Additionally, other parameters, such as gradient magnitude,
are often retrieved for each sample point. To find a good transfer
function for this high number of parameters is challenging because
of the complexity of this task. In this paper we present a general
informationbased approach for transfer function design in multimodal
visualization which is independent of the used modality types. Based
on information theory, the complex multidimensional transfer function
space is fused to allow utilization of a wellknown 2D transfer function
with a single value and gradient magnitude as parameters. Additionally,
a quantity is introduced which enables better separation of regions
with complementary information. The benefit of the new method in
contrast to other techniques is a transfer function space which is
easy to understand and which provides a better separation of different
tissues. The usability of the new approach is shown on examples of
different modalities.},
isbn = {9783905674132},
issn = {20705778},
keywords = {multimodal visualization, transfer functions, information theory},
location = {Delft},
url = {http://www.cg.tuwien.ac.at/research/publications/2008/haidacher2008vcbm/}
}
