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

7.3. Evaluation and Results 115
Grouping and linkage: For reconstruction of the airway tree the structure base approach
as presented in Section 3.2 is utilized that is used to identify the tubular structures
that are part of the actual airway tree and to discard all other unrelated tubular structures.
The method requires information about the root of the tree and the flow direction
inside this root element. Therefore, the trachea is identified automatically by searching
for the largest tubular structure, it’s flow direction is determined as pointing towards the
center of the volume. The result of the tree reconstruction process is shown in Figs. 7.4(e)
and (f).
Parameters: Following set of parameters is used to process the datasets. The tube
detection is performed on 15 radius steps on a logarithmic scale between radii 0.25 mm and
10 mm with η = 0.7 (the variance term of the boundariness samples in the offset medialness
function was omitted for radii ≤ 0.5 mm); t high = 35, t low = 25, and t conf = 150 for the
centerline extraction; ρ = 0.5, γ a = 90 ◦ , γ r = 1.3, γ d = 40 mm, and γ c = 0.1 for the tree
reconstruction.
7.3 Evaluation and Results
Both approaches were applied to 40 clinical datasets (with undisclosed gold standard)
which were provided by the “Extraction of Airways from CT 2009 (EXACT09)” database
(http://image.diku.dk/exact), whose objective is the quantitative evaluation and comparison
of airway tree extraction methods. The focus of the database is on the methods
ability to successfully extract the structure of the airway trees, while surface accuracy is
not considered. The datasets are split in two groups of 20 training datasets, where the
parameters are adapted and 20 testing datasets. For information about how the reference
segmentations were obtained and the exact definition of the used performance measures
we refer to Lo et al. [88].
For evaluation, binary volume datasets are required that contain a single 6-connected
airway structure. While the GVF based method (Section 7.2.1) already provides such
a segmentation, the airway tree reconstruction method (Section 7.2.2) only produces a
26-connected airway tree skeleton with corresponding radius information. Thus, to obtain
a binary volume dataset for the airway tree reconstruction method, we performed an
inverse distance transformation to obtain a rough segmentation and dilated the so obtained
reconstruction to assure 6-connectivity.
In the next two sections, we present quantitative results achieved with our two pre-