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Information Theory-Based Automatic Multimodal Transfer Function Design

In this paper, we present a new framework for multimodal volume visualization that combines several informationtheoretic strategies to define both colors and opacities of the multimodal transfer function. To the best of our knowledge, this is the first fully automatic scheme to visualize multimodal data. To define the fused color, we set an information channel between two registered input data sets and, afterwards, we compute the informativeness associated with the respective intensity bins. This informativeness is used to weight the color contribution from both initial 1D transfer functions. To obtain the opacity, we apply an optimization process that minimizes the informational divergence between the visibility distribution captured by a set of viewpoints and a target distribution proposed by the user. This distribution is defined either from the data set features, from manually set importances, or from both. Other problems related to the multimodal visualization, such as the computation of the fused gradient and the histogram binning, have also been solved using new information-theoretic strategies. The quality and performance of our approach is evaluated on different data sets

This work was supported in part by the Spanish Government under Grant TIN2010-21089-C03-01, the Catalan Government under Grant 2009-SGR-643, and by SUR of DEC of Generalitat de Catalunya (Catalan Government)

© IEEE Transactions on Information Technology in Biomedicine, vol. 17, núm. 4, p. 870-880

IEEE

Author: Bramon Feixas, Roger
Ruiz Altisent, Marc
Bardera i Reig, Antoni
Boada, Imma
Feixas Feixas, Miquel
Sbert, Mateu
Date: 2013 July
Abstract: In this paper, we present a new framework for multimodal volume visualization that combines several informationtheoretic strategies to define both colors and opacities of the multimodal transfer function. To the best of our knowledge, this is the first fully automatic scheme to visualize multimodal data. To define the fused color, we set an information channel between two registered input data sets and, afterwards, we compute the informativeness associated with the respective intensity bins. This informativeness is used to weight the color contribution from both initial 1D transfer functions. To obtain the opacity, we apply an optimization process that minimizes the informational divergence between the visibility distribution captured by a set of viewpoints and a target distribution proposed by the user. This distribution is defined either from the data set features, from manually set importances, or from both. Other problems related to the multimodal visualization, such as the computation of the fused gradient and the histogram binning, have also been solved using new information-theoretic strategies. The quality and performance of our approach is evaluated on different data sets
This work was supported in part by the Spanish Government under Grant TIN2010-21089-C03-01, the Catalan Government under Grant 2009-SGR-643, and by SUR of DEC of Generalitat de Catalunya (Catalan Government)
Format: application/pdf
ISSN: 1089-7771 (versió paper)
1558-0032 (versió electrònica)
Document access: http://hdl.handle.net/10256/8795
Language: eng
Publisher: IEEE
Collection: MICINN/PN 2011-2013/TIN2010-21089-C03-01
AGAUR/2009-2014/2009 SGR-643
Versió postprint del document publicat a: http://dx.doi.org/10.1109/JBHI.2013.2263227
Articles publicats (IIIA)
Is part of: © IEEE Transactions on Information Technology in Biomedicine, vol. 17, núm. 4, p. 870-880
Rights: Tots els drets reservats
Subject: Imatge, Tècniques d’
Imaging systems
Visualització (Informàtica)
Information display systems
Title: Information Theory-Based Automatic Multimodal Transfer Function Design
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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