Skeletonization of Volumetric Vascular Images—Distance Information Utilized for Visualization

Abstract

This paper deals with two techniques to represent relevant information from volumetric vascular images in a more compact format. The images are obtained with contrast-enhanced magnetic resonance angiography (MRA). After segmentation of the vessels, the curve skeleton is extracted by an algorithm based on the distance transformation. The algorithm first reduces the original object to a surface skeleton and then to a curve skeleton, after which “pruning” can be performed to remove irrelevant small branches. Applying this procedure to MRA data from the pelvic arteries resulted in a good description of the tree structure of the vessels with a much smaller number of voxels. To detect stenoses, 2D projections such as maximum intensity projection (MIP) are usually employed, but these often fail to demonstrate a stenosis if the projection angle is not suitably chosen. A new presentation method surrounds each voxel in the distance-labeled curve skeleton of the segmented vascular tree with a ball whose radius represents the minimum vessel radius at that level. Experiments with synthetic data indicate that stenoses invisible in an ordinary projection may be seen with this technique. It is concluded that the distance-labelled curve skeleton seems to be useful for visualizing variations in vessel calibre and in the future possibly also for quantification of arterial stenoses.