PROCEEDINGS ARTICLE | February 14, 2012
Proc. SPIE. 8314, Medical Imaging 2012: Image Processing
KEYWORDS: Detection and tracking algorithms, Tissues, Image segmentation, Scanners, Arteries, Detector development, Angiography, Computed tomography, Algorithm development, Clinical research
Endovascular imaging aims at identifying vessels and their branches. Automatic vessel segmentation and bifurcation
detection eases both clinical research and routine work. In this article a state of the art bifurcation
detection algorithm is developed and applied on vascular computed tomography angiography (CTA) scans to
mark the common iliac artery and its branches, the internal and external iliacs.
In contrast to other methods our algorithm does not rely on a complete segmentation of a vessel in the
3D volume, but evaluates the cross-sections of the vessel slice by slice. Candidates for vessels are obtained by
thresholding, following by 2D connected component labeling and prefiltering by size and position. The remaining
candidates are connected in a squared distanced weighted graph. With Dijkstra algorithm the graph is traversed
to get candidates for the arteries. We use another set of features considering length and shape of the paths to
determine the best candidate and detect the bifurcation.
The method was tested on 119 datasets acquired with different CT scanners and varying protocols. Both
easy to evaluate datasets with high resolution and no apparent clinical diseases and difficult ones with low
resolution, major calcifications, stents or poor contrast between the vessel and surrounding tissue were included.
The presented results are promising, in 75.7% of the cases the bifurcation was labeled correctly, and in 82.7% the
common artery and one of its branches were assigned correctly. The computation time was on average 0.49 s ±
0.28 s, close to human interaction time, which makes the algorithm applicable for time-critical applications.
