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greater than 82.10 %. However, the testing images had to be manually labeled with
the boundary points.
Later on, Koompairojn et al. [
56
] described a fully automatic Spinal Stenosis
diagnosis system via vertebral morphometry [
73
], using an Active Appearance
Model (AAM) for segmentation and a Bayesian framework for classi
cation.
Experimental results on 86 lumbar spine X-ray images from the NHANES II
database showed accuracy ranging from 75 to 80 %. Moreover, Stanley et al. [
89
]
investigated new size-invariant features (claw and traction) for the detection of
anterior Osteophytes for ef
cient Content Based Image Retrieval (CBIR). Using a
K-means clustering and nearest neighbor classi
cation approach, average correct
classi
cation rates of 85.80, 86.04 and 84.44 % were obtained for claw, traction and
anterior Osteophytes, respectively, on 390 cervical vertebrae.
5.2 Computed Tomography (CT)
CT imaging technique has become indispensable for diagnosis of spine abnor-
malities by providing a detailed 3D representation of the anatomy. Compared to X-
ray radiography and MR imaging, CT proved to have higher sensitivity and
specificity in the visualization of the bone structures.
5.2.1 Vertebrae
A number of automatic and semi-automatic methods for segmentation of vertebrae
and vertebral structures in CT have been proposed [
27
,
50
,
52
,
64
,
80
,
84
,
91
,
96
,
98
,
102
] over the last decade. On one hand some researchers proposed techniques
that segment each vertebra separately [
50
,
61
,
64
,
84
,
98
], which might lead to mis-
segmentation due to absence of a clear boundary between vertebrae. To overcome
this issue, some authors proposed techniques for simultaneous segmentation of all
vertebrae [
52
,
80
].
Early last decade, Hahn [
36
], proposed a fully automated approach to evaluate
rotation of the cervical vertebrae in 3D using a multidimensional Powell minimi-
zation algorithm for spiral CT scans. Later, in 2004, the same research group [
37
]
presented a method for determination of the planes separating the individual ver-
tebrae of the spine from CT volumes using a Balloon based model. This model
requires careful initialization similar to the 2D active contours (snakes) besides its
high dependency on the edge detector.
Meanwhile, Ghebreab and Smeulders [
27
] presented a combination of Strings
[
26
] and Necklaces [
28
] to model the spine in the lumbar area using both a priori
knowledge about natural variation and anatomical saliency in the visual appearance
of the spine. The Strings model focuses on learning the most relevant biological
variation in the visual appearance of the spine as a whole, and Necklaces aims at
exploiting inhomogeneities in multiple continuous shape and gray-level features of
