Skeletonization of volumetric vascular images - Distance information utilized for visualization

This paper deals with two techniques to represent relevant information from volumetric vascular images in a more compact format. The images are obtain ed with contrast-enhanced magnetic resonance angiography (MRA). After segme ntation of the vessels, the curve skeleton is extracted by an algorithm bas ed on the distance transformation. The algorithm first reduces the original object to a surface skeleton and then to a curve skeleton, after which "pr uning" can be performed to remove irrelevant small branches. Applying this procedure to MRA data from the pelvic arteries resulted in a good descripti on of the tree structure of the vessels with a much smaller number of voxel s. 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 s urrounds each voxel in the distance-labeled curve skeleton of the segmented vascular tree with a ball whose radius represents the minimum vessel radiu s at that level. Experiments with synthetic data indicate that stenoses inv isible in an ordinary projection may be seen with this technique. It is con cluded that the distance-labelled curve skeleton seems to be useful for vis ualizing variations in vessel calibre and in the future possibly also for q uantification of arterial stenoses.