Title
Parameterization, Feature Extraction and Binary Encoding for the Visualization of Tree‐Like Structures
Abstract
The study of vascular structures, using medical 3D models, is an active field of research. Illustrative visualizations have been applied to this domain in multiple ways. Researchers made the geometric properties of vasculature more comprehensive and augmented the surface with representations of multivariate clinical data. Techniques that head beyond the application of colour-maps or simple shading approaches require a surface parameterization, that is, texture coordinates, in order to overcome locality. When extracting 3D models, the computation of texture coordinates on the mesh is not always part of the data processing pipeline. We combine existing techniques to a simple parameterization approach that is suitable for tree-like structures. The parameterization is done w.r.t. to a pre-defined source vertex. For this, we present an automatic algorithm, that detects the tree root. The parameterization is partly done in screen-space and recomputed per frame. However, the screen-space computation comes with positive features that are not present in object-space approaches. We show how the resulting texture coordinates can be used for varying hatching, contour parameterization, display of decals, as additional depth cues and feature extraction. A further post-processing step based on parameterization allows for a segmentation of the structure and visualization of its tree topology.
Year
DOI
Venue
2020
10.1111/cgf.13888
COMPUTER GRAPHICS FORUM
Keywords
Field
DocType
non-photorealistic rendering,rendering,scientific visualization,visualization,center dot Computing methodologies -> Non-photorealistic rendering,Mesh geometry models
Computer vision,Binary encoding,Parametrization,Computer science,Visualization,Feature extraction,Non-photorealistic rendering,Artificial intelligence,Rendering (computer graphics),Scientific visualization
Journal
Volume
Issue
ISSN
39.0
1.0
0167-7055
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Nils Lichtenberg122.08
Kai Lawonn219929.75