Segmentation of 3D Tubular Tree Structures in Medical Images ...

102 Chapter 6. Coronary Artery Tree Extraction
what forms a kind of coned region (with an opening angle specified by ρ) pointing away
from the centerline where the method searches for continuations; potential connections
with too large costs C > c max are discarded. The final reconstructed trees (after removal
of the non-coronary artery trees; see next paragraph) is shown in Fig. 6.1(e).
Coronary artery tree identification: The method as described above also extracts
the centerlines of other tubular objects (see Fig. 6.1). Thus, not just the coronary arteries
are identified, but also other structures such as blood vessels in the lung and chest area,
the aorta and also some of the bones and an identification of the coronary artery trees is
necessary. The lung tissue is identified and removed based on thresholding (I(x) < −700
HU) and morphological closing using a spherical structuring element with a radius of 10
voxels; see Fig. 6.1(c) for the TDF response after removal of the lung tissue. Other tree
structures not belonging to the coronary artery trees are typically isolated and relatively
small. Thus, the coronary artery trees can be identified as the two largest connected
components (regarding their centerline length). An example of the identified coronary
artery trees is shown in Fig. 6.1(e).
Parameters: Following set of parameters is used to process the datasets. F max = 100,
t high = 0.5, t low = 0.1, and l min = 10 voxels for the extraction of tubular structures with
µ = 5 and 500 iterations for the GVF computation [168] and ρ = 0.7, d max = 200 HU,
and c max = 20 for the grouping and linkage.
6.3 Evaluation and Results
Our approach was evaluated on 32 coronary CTA datasets from the “Rotterdam Coronary
Artery Algorithm Evaluation Framework” (http://coronary.bigr.nl), whose goal is
the quantitative evaluation and comparison of methods for the coronary artery centerline
extraction based on a set of well defined performance measures. The datasets are split
in two groups of 8 training datasets with provided reference where the parameters have
been adapted and 24 testing datasets with undisclosed reference centerlines. For each of
the datasets, reference centerlines of four vessels are available that were obtained based on
manual annotation and reference points that unambiguously identify the corresponding
vessels. In Fig. 6.3 the coronary artery trees and provided reference centerlines are shown.
Based on these provided reference points the centerlines of the associated arteries were
selected from the coronary artery trees – which are extracted completely with our approach