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

110 Chapter 7. Airway Tree Segmentation
on density/gray-value information for airway segmentation, some approaches focus on
airway candidate detection using mathematical morphology [37], template matching techniques
[136], or voxel classification based on different image descriptors [86, 122]. Many
of the available approaches have problems in dealing with local disturbances (e.g., motion
artifacts) or pathology (e.g., obstructed airway) which may result in incomplete airway
segmentations. Graham et al. [50] addressed this problem using a different approach by
building an airway tree from candidate airway branch segments by computing connection
costs between branches and using graph theoretic approaches to extract the airway
tree [50].
In this chapter, we present two different methods for the extraction of airways trees
from CT datasets with different properties. The first method performs a segmentation of
the airway tree based on the GVF, while the second method performs a reconstruction of
the airway tree from detected/extracted tubular objects based on structural properties.
Both approaches have been evaluated on a public database. Results as well as a comparison
to the results achieved with other methods are presented and discussed.
7.2 Methods
7.2.1 Airway Tree Segmentation Based on Gradient Vector Flow
In this section, we present an automated approach for segmentation of airway trees based
on the GVF. The method produces accurate segmentations of the airway lumen and the
obtained GVF field may be directly utilized to obtain a high-quality skeleton. The method
follows the general approach for segmentation of branched tubular networks as outlined in
Section 1.2. However, the grouping step is skipped as it is not necessary for this task. The
method consists of four processing steps: deriving an appropriate initial vector field and
applying the GVF, extraction of tubular structures from the GVF field, segmentation of
the identified tubular structures using the GVF field, and post processing for identification
of the airway tree. Intermediate processing results are depicted in Figs. 7.1, 7.2, and 7.3.
Gradient Vector Flow:
The extraction of the tubular objects as well as the segmentation
parts utilize the GVF field V (x) resulting from the following initial vector
field that is required if one is interested in dark structures as explained in Section 2.3:
F n (x) = F (x) min(|F (x)|,F max)
|F (x)| F max
with F = −∇(G σ ⋆ I), where I is the original image and G σ
is a Gaussian filter kernel at scale σ. An example of applying it to a thorax CT dataset is