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.
In this paper, we evaluate a novel image registration method on a set of expiratory-inspiratory pairs of computed
tomography (CT) lung scans. A free-form multi resolution image registration technique is used to match two
scans of the same subject. To account for the differences in the lung intensities due to differences in inspiration
level, we propose to adjust the intensity of lung tissue according to the local expansion or compression. An image
registration method without intensity adjustment is compared to the proposed method. Both approaches are
evaluated on a set of 10 pairs of expiration and inspiration CT scans of children with cystic fibrosis lung disease.
The proposed method with mass preserving adjustment results in significantly better alignment of the vessel
trees. Analysis of local volume change for regions with trapped air compared to normally ventilated regions
revealed larger differences between these regions in the case of mass preserving image registration, indicating
that mass preserving registration is better at capturing localized differences in lung deformation.