DNA Image Cytometry is a method for non-invasive cancer diagnosis which measures the DNA content of
Feulgen-stained nuclei. DNA content is measured using a microscope system equipped with a digital camera as
a densitometer and estimating the DNA content from the absorption of light when passing through the nuclei.
However, a DNA Image Cytometry measurement is only valid if each nucleus is only measured once.
To assist the user in preventing multiple measurements of the same nucleus, we have developed a unique
digital identifier for the characterization of Feulgen-stained nuclei, the so called Nucleus Fingerprint. Only nuclei
with a new fingerprint can be added to the measurement. This fingerprint is based on basic nucleus features,
the contour of the nucleus and the spatial relationship to nuclei in the vicinity. Based on this characterization,
a classifier for testing two nuclei for identity is presented.
In a pairwise comparison of ≈40000 pairs of mutually different nuclei, 99.5% were classified as different. In
another 450 tests, the fingerprints of the same nucleus recorded a second time were in all cases judged identical.
We therefore conclude that our Nucleus Fingerprint approach robustly prevents the repeated measurement of
nuclei in DNA Image Cytometry.
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.