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

8 Chapter 1. Introduction
features based on 2D cross-sectional measurements that require the orientation as an input
parameter [2, 40, 41, 46, 64, 141, 162]. Similarly as with the different models, bifurcations
or anomalies are often not considered explicitly. However, exceptions – typically adapted
to specific diseases – can be found in [1, 45, 83].
Extraction Schemes: The extraction scheme represents the actual segmentation algorithm
– they are based on the model assumptions and are guided by the extracted features.
Lesage et al. [79] classify methods known from the literature into three general classes of
extraction schemes as well as pre- and post-processing techniques.
For pre-processing, several vessel-specific methods can be found in the literature. Several
of the features described above were originally presented as vessel enhancement filters
[1, 44, 64, 70, 83, 121], while similar formulations may be incorporated into vessel-dedicated
anisotropic diffusion schemes [66, 94, 140]. Other typical pre-processing tasks involve the
generation of pre-segmentations such as generation of regions of interest based on prior
knowledge [39, 111, 131] or based on thresholded potential maps derived from probability
maps from appearance models or from vessel enhanced images [1, 23, 161, 167, 170]. To
select sparse sets of vessel-candidate points robust maxima [138] or local maxima are used
[39, 75].
For the actual extraction scheme one may distinguish between region-growing algorithms,
active-contour based methods, and centerline based approaches. Region-growing
or wave-propagation methods start from a seed point or region inside the vessel and merge
neighboring voxels in a greedy way using various merging criteria [14, 19, 62, 117, 175].
More advanced techniques of this category adapt parameters during growing or analyze
the local segmentation results to avoid segmentation errors [62, 93, 101, 117, 146, 175].
Active-contour based methods evolve an interface following external data-driven and internal
model-driven forces, using explicit (classical snakes) [28, 42, 59, 96, 103] or implicit
representations (level-sets) [60, 61, 90, 91, 106]. Within this category several vessel-specific
adaptions have been presented in the literature to handle the complex topology of vascular
systems (topology-adaptive snakes) [97, 98], to incorporate vessel-specific features
exploiting the vessels direction (eigen-snakes, curves) [90, 144, 145], or they are based
on curvature-based regularization [60, 155, 169]. The last category of extraction schemes
are centerline based approaches, that in contrast to the other methods do not focus on
detecting the vessels contour using pixel-wise criteria, but on extraction of the vessels
centerlines directly from the image data. Therefore, direct centerline tracking methods
[1, 2, 42, 45, 48, 141] have been presented that track starting from a seed along a vessel