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Fig. 5.10 Comparison of the
resulting segmentation for
datasets #3, #5 and #11
obtained using the proposed
model (a), (c) and (e) and
Yang et al. [ 16 ] technique
(b), (d) and (f), with respect
to the ground truth data. The
semi-transparent surface
denotes the outer surface of
the ground truth data, and the
resulting segmentation is
depicted in red
boundary, and the blue and black curves represent the segmentation obtained using
the proposed method and Yang et al. [ 16 ] algorithm, respectively. Since the ground
truth data were modelled as circular cross-sectional tubes, this would lead to the
resulting ground truth data under-estimating the true vessel surfaces. However, in
terms of the voxel-wise measurements, the average value of the Hausdorff distance
metric was found to be 0.73 voxels, which implies that the proposed method is
capable of extracting the luminal surfaces of the arteries with sub-voxel accuracy.
In terms of the FP rate, the Yang et al. [ 16 ] method outperforms the proposed
approach, with the average value found to be 16.9 %. However, we note that the
TP and OM metrics of their segmentation results are significantly lower than those
of the proposed technique, with the average values being 53.8 % and 0.556,
respectively. In addition, by observing the statistics presented in Tables 5.2 and
5.3 , we note that both the TP rate and the OM metric vary significantly, with the
TP rate ranging from 23.4 to 89.9 %, while the minimum and maximum values of
the OM metric were found to be 0.318 and 0.717, respectively. These observations
imply that Yang et al. [ 16 ] model under-segments the coronary arteries and is only
able to extract partial branches of the arterial tree, which is illustrated in
Figs. 5.10 b, d, e. The reason for this is that the technique, solely relying on global
intensity statistics, is sensitive to image contrast and brightness changes. The
method is able to correctly extract the arteries when the intensity densities are
evenly distributed along the vessel. However, uneven intensity distribution is
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