Issue No. 09 - Sept. (2012 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.280
M. Feixas , Univ. of Girona, Girona, Spain
J. Rodriguez , Univ. of Girona, Girona, Spain
A. Bardera , Univ. of Girona, Girona, Spain
I. Boada , Univ. of Girona, Girona, Spain
R. Bramon , Univ. of Girona, Girona, Spain
J. Puig , Hosp. Josep Trueta of Girona, Girona, Spain
M. Sbert , Univ. of Girona, Girona, Spain
Multimodal visualization aims at fusing different data sets so that the resulting combination provides more information and understanding to the user. To achieve this aim, we propose a new information-theoretic approach that automatically selects the most informative voxels from two volume data sets. Our fusion criteria are based on the information channel created between the two input data sets that permit us to quantify the information associated with each intensity value. This specific information is obtained from three different ways of decomposing the mutual information of the channel. In addition, an assessment criterion based on the information content of the fused data set can be used to analyze and modify the initial selection of the voxels by weighting the contribution of each data set to the final result. The proposed approach has been integrated in a general framework that allows for the exploration of volumetric data models and the interactive change of some parameters of the fused data set. The proposed approach has been evaluated on different medical data sets with very promising results.
sensor fusion, content management, data visualisation, medical computing, medical data sets, mutual information-based multimodal data fusion, multimodal data visualization, information-theoretic approach, informative voxels, information channel, intensity value, mutual information decomposition, assessment criterion, information content, volumetric data models exploration, Transfer functions, Data visualization, Rendering (computer graphics), Mutual information, Biomedical imaging, Image color analysis, Data models, mutual information., Multimodal visualization, image fusion, information theory
M. Feixas et al., "Multimodal Data Fusion Based on Mutual Information," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 1574-1587, 2012.