We describe a pipeline of image processing steps for deriving symbolic models of vascular structures from radiological data which reflect the branching pattern and diameter of vessels. For the visualization of these symbolic models, concatenated truncated cones are smoothly blended at branching points. We put emphasis on the quality of the visualizations which is achieved by anti-aliasing operations in different stages of the visualization. The methods presented are referred to as HQVV (High Quality Vessel Visualization). Scalable techniques are provided to explore vascular structures of different orders of magnitude. The hierarchy as well as the diameter of the branches of vascular systems are used to restrict visualizations to relevant subtrees and to emphasize parts of vascular systems.Our research is inspired by clear visualizations in textbooks and is targeted toward medical education and therapy planning. We describe the application of vessel visualization techniques for liver surgery planning. For this application it is crucial to recognize the morphology and branching pattern of vascular systems as well as the basic spatial relations between vessels and other anatomic structures.
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