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Context-Preserving Rendering of Medical Segmentation Data

Authors: David, Hughes; Ik Soo, Lim;

Context-Preserving Rendering of Medical Segmentation Data

Abstract

We present a context-preserving visualisation method for segmented volumetric medical images. A segmented volumetric image contains a number of anatomical objects which are important features to be visualised. Our context-preserving rendering utilises the curvature at the surfaces of the segmentation objects to modulate the opacity contribution during rendering. This results in the areas of high curvature, typically the most important features, being opaque and visible while everything else being transparent.

Related Organizations
Keywords

Models, Anatomic, Reproducibility of Results, Image Enhancement, Models, Biological, Sensitivity and Specificity, Pattern Recognition, Automated, Imaging, Three-Dimensional, Artificial Intelligence, Subtraction Technique, Image Interpretation, Computer-Assisted, Computer Simulation, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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