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Spatial Conditioning of Transfer Functions Using Local Material Distributions
November/December 2010 (vol. 16 no. 6)
pp. 1301-1310
ASCII Text
x 
 
Stefan Lindholm, Patric Ljung, Claes Lundström, Anders Persson, Anders Ynnerman, "Spatial Conditioning of Transfer Functions Using Local Material Distributions," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1301-1310, November/December, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2010.195,
author = {Stefan Lindholm and Patric Ljung and Claes Lundström and Anders Persson and Anders Ynnerman},
title = {Spatial Conditioning of Transfer Functions Using Local Material Distributions},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {16},
number = {6},
issn = {1077-2626},
year = {2010},
pages = {1301-1310},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.195},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Spatial Conditioning of Transfer Functions Using Local Material Distributions
IS - 6
SN - 1077-2626
SP1301
EP1310
EPD - 1301-1310
A1 - Stefan Lindholm,
A1 - Patric Ljung,
A1 - Claes Lundström,
A1 - Anders Persson,
A1 - Anders Ynnerman,
PY - 2010
KW - Direct Volume Rendering
KW - Transfer Function
KW - Spatial Conditioning
KW - Neighborhood Meta-Data.
VL - 16
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Stefan Lindholm, C-Research - Linköping University, Sweden and Siemens Corporate Research, USA
Patric Ljung, Siemens Corporate Research, USA
Claes Lundström, Sectra Imtec AB, Sweden
Anders Persson, CMIV - Link öping University - Linköping University Hospital, Sweden
Anders Ynnerman, C-Research - Linköping University, Sweden
In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patient's symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the user's specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.

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Index Terms:
Direct Volume Rendering, Transfer Function, Spatial Conditioning, Neighborhood Meta-Data.
Citation:
Stefan Lindholm, Patric Ljung, Claes Lundström, Anders Persson, Anders Ynnerman, "Spatial Conditioning of Transfer Functions Using Local Material Distributions," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1301-1310, Nov.-Dec. 2010, doi:10.1109/TVCG.2010.195
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