Information Technology Reference
In-Depth Information
vertebrae. Thus they were able to use both a priori knowledge about natural vari-
ation and anatomical saliency in the visual appearance of the spine. However, they
tested their method on only six CT cases and with minimal spinal and vertebral
deformations. Furthermore, manual intervention is used for initialization of their
model. On the other hand, Vrtovec et al. [
96
] detected the spine curve from CT
using a polynomial model to provide a Curved Planar Reformation (CPR) of the 3D
spinal column. They
fit the spinal curve to a set of points extracted from a distance
map that emphasized the vertebral bodies and tested the method on
ve cases,
including one Scoliotic case achieving mean positional errors between 2 and 6 mm.
Furthermore, Yao et al. [
102
] presented a systematic algorithm for segmenting
the spinal column from chest and/or abdominal CT scans, without labeling of
vertebrae. Their method is based on thresholding, Watershed, and directed graph
search besides modeling the vertebral bony tissue as a four-part model. They
showed correct segmentation of 69 cases out of the 71 total cases. Meanwhile,
Mastmeyer et al. [
64
] segmented the lumbar vertebral bodies in CT images by
combining viscous deformable models with the geometrical shape of the vertebral
body, starting from a point in the center of each vertebral body. Tan et al. [
91
]
presented a level set-based segmentation algorithm for the vertebrae and validated
their work on synthetic 3D vertebrae volumes. After parameter selection, they
tested the algorithm on 50 vertebrae (from ten subjects), obtaining 90 % success
rate. Later, Shen et al. [
84
] presented a segmentation technique for vertebrae from
3D CT scans using prior knowledge with a set of high level features to form a
surface model. However, they did not perform labeling. They tested their model on
150 vertebrae with a comparative segmentation to two experts
segmentation. In the
same year, Klinder et al. [
51
] presented a two-scale framework for modeling and
segmenting the spine from thoracic CT scans, achieving a segmentation accuracy of
1.0 mm in average for ten thoracic CT volumes. By applying statistical models of
shape, gradient and appearance of spinal structures in 3D, the same research group
[
52
] detected, identi
'
ed and segmented the vertebrae in CT volumes. However,
their identi
cation algorithm is based on vertebrae Active Appearance Model for
spatial
registration and matching which is very computationally expensive
(20
30 min per case). Their framework was tested on 64 CT images including
pathologies like Scoliosis, Kyphosis and collapsed vertebrae. Later, Kim and
Kim [
50
] automatically segmented the vertebrae by a region growing algorithm
inside a volume limited by a 3D fence that was obtained from a deformable model.
They obtain 80 % success on a 50 patient dataset. More recently, Ma and Lu [
61
]
proposed a method for segmentation and identi
-
cation of thoracic vertebrae in CT
images by training an edge detector to bone structures via steerable gradient fea-
tures and using a deformable surface model in a two-stage coarse-to-
ne scheme.
±
They achieve point-to-surface error 0.95
0.91 mm on 40 volumes.
Segmentation that is not based on deformable models [
50
,
91
,
102
], generally do
not provide any quantitative information of vertebral deformations for CAD sys-
tems, while the segmentation based on deformable models is mathematically too
abstract for describing deformations in clinical practice [
52
,
61
,
64
]. For example,
Š
