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

98 Chapter 6. Coronary Artery Tree Extraction
follow a tracking paradigm, iteratively expanding already known coronary artery parts
starting from a single seed. To the best of our knowledge, the only two exceptions are
the work of Szymszack et al. [138] and Florin et al. [39]. Both identify candidate vessel
locations using persistent maxima in single image slices and a normal MIP-raycasting technique,
respectively. These candidate vessel locations are merged bottom-up into complete
tree structures using a minimum spanning tree approach. The method of Florin et al. [39]
requires a heart segmentation to limit the vessel candidate detection to a small region
at the hearts surface and to avoid false candidates. The candidate selection approach of
Szymszack et al. [138] is not very discriminative such that the method requires additional
pruning steps and a selection of the coronary artery tree to remove false positives.
In this chapter, we present an automated approach for the extraction of coronary artery
trees from cardiac CTA datasets obtaining curve skeletons for the two largest coronary
trees. The approach has been evaluated on a publicly available database. Results as well
as a comparison to the results achieved with other methods are presented and discussed.
6.2 Method
The method follows our general approach for segmentation of branched tubular networks
as outlined in Section 1.2 to achieve a high robustness against disturbances. It combines
tree main processing steps for extraction of the coronary artery trees centerlines: an
extraction of all tubular structures, a grouping and linkage of the tubular structures, and
discarding centerlines not belonging to the coronary arteries based on prior knowledge.
The segmentation step of our general approach is skipped as accurate segmentations are
not required. An exemplary dataset with intermediate processing results is shown in
Fig. 6.1.
Extraction of tubular structures: For identification of tubular structures the GVF
based TDF with the central medialness function as presented in Section 2.3.1 is utilized.
The required initial vector field F n is obtained from an edge potential image to be able
to handle varying background conditions (e.g. calcifications) after median filtering of the
original dataset (filter size 5 × 5 × 5 voxels). Applying this approach for detection of
coronary arteries on heart CTA datasets results in the response shown in Fig. 6.2(c),
whereas applying Frangi’s vesselness measure to the original vector field F n results in
the response shown in Fig. 6.2(b). The GVF based approach performs well in identifying
major parts of the coronary arteries independent of their size and produces clean responses