Information Theory-Based Automatic Multimodal Transfer Function Design
R. Bramon,M. Ruiz,Anton Bardera,Imma Boada,Miquel Feixas,Mateu Sbert +5 more
- 15 May 2013
- Vol. 17, Iss: 4, pp 870-880
TL;DR: This paper presents a new framework for multimodal volume visualization that combines several information-theoretic strategies to define both colors and opacities of the multi-D transfer function, and is the first fully automatic scheme to visualize multimodAL data.
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Abstract: In this paper, we present a new framework for multimodal volume visualization that combines several information-theoretic 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 datasets, and afterward, we compute the informativeness associated with the respective intensity bins. This informativeness is used to weight the color contribution from both initial 1-D 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 dataset 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 are evaluated on different datasets.
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Figures
Fig. 4. CT and MR head data sets of Figure 1(a) and 1(d) are shown after applying the binning step with (a) 16 bins and (b) 32 bins. 
TABLE I TIME COST IN SECONDS REQUIRED FOR THE MAIN STEPS OF THE FUSION PROCESS. TARGET DISTRIBUTIONS ARE: (1) OCCURRENCE, (2) OCCURRENCE WEIGHTED BY GRADIENT, (3) OCCURRENCE WEIGHTED BY IMPORTANCE, AND (4) OCCURRENCE WEIGHTED BY GRADIENT AND IMPORTANCE. 
Fig. 5. From left to right, the representation of the gradient magnitudes of the input CT and MR head data sets, and the fused data set. 
Fig. 11. Multimodal visualization of a dual energy CT scan of a power connector with the target of occurrence weighted by gradient considering (ab) one view, (c-d) 6 views and (e-f) 20 views. 
Fig. 1. From left to right, the original CT and MR head data sets and their corresponding I2 and I3 information maps. 
Fig. 6. Multimodal visualization of (i.a) CT and (ii.a) MR data sets using different target distributions: (b) occurrence, (c) occurrence weighted by gradient, and (d) occurrence weighted by gradient and importance. Results (i.b-d) are obtained using 16 non-uniform intensity clusters for each data set and 32 uniform bins for the gradient magnitude, and (ii.b-d) using 32 non-uniform intensity clusters for each data set and 8 uniform bins for the gradient magnitude.
Citations
State of the Art in Transfer Functions for Direct Volume Rendering
Patric Ljung,Jens Krüger,Eduard Groller,Markus Hadwiger,Charles Hansen,Anders Ynnerman +5 more
- 01 Jun 2016
TL;DR: The purpose of this state‐of‐the‐art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations.
Occlusion and Slice-Based Volume Rendering Augmentation for PET-CT
TL;DR: A new visualization algorithm is proposed where an SOI from PET is augmented by volumetric contextual information from a DVR of the counterpart CT so that the obtrusiveness from the CT in the SOI is minimized.
Generalized temporal focus + context framework for improved medical data exploration.
TL;DR: A generalized temporal focus + context framework that unifies different widely used and novel visualization methods is described and two novel framework-based techniques that support improved planning of procedures that involve drilling or endoscopic exploration are described.
Image-Based TF Colorization With CNN for Direct Volume Rendering
TL;DR: In this paper, an image-based TF colorization with CNN is proposed to automatically generate a direct volume rendering image (DVRI) similar to a target image, which is then used to generate a DVRI similar to the target image.
The Virtual Surgical Pelvis : Anatomy Visualization for Education and Surgical Planning
N.N. Smit
- 31 Oct 2016
TL;DR: The core contribution of this work is a taxonomy based on the multimodal medical visualization applications so far, the visualization techniques they employ, and the medical domain context, which provides an outlook on open problems and potential future research directions.
5
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