Information Technology Reference
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In most of the previous work, segmentation of the Dural Sac, vertebrae and
intervertebral discs have been handled separately which might lead to overlapping
tissue regions. Moreover, some techniques depend on shape models giving rise to
errors in case of high variability in appearance. Recently Ghosh et al. [
34
], used a
Gibbs sampling approach to simultaneously label all tissues in the lumbar MRI.
This method uses both neighborhood intensity information and label information
for each update. Experimental results on 53 cases showed an average Similarity
Index of 0.77 and 0.66 for the Dural Sac and Intervertebral discs respectively.
Within the same research group, Alomari et al. [
6
] presented a coordinated joint
model to accurately segment the lumbar discs from clinical MRIs in addition to
their diagnosis work.
On the other hand, due to better discrimination of soft tissues in MRI, there has
been a growing interest in the research community for automatic diagnosis of
InterVertebral Discs (IVD) abnormalities such as Herniation, Degeneration, Des-
iccation, as well as Spinal Stenosis and Spinal Scoliosis from 2D and 3D MRIs.
Most of these efforts include steps for localization and segmentation of the target
structure.
Early last decade, Tsai et al. [
94
] detected Herniation from 3D MRI and CT
volumes of the discs by using geometric features such as shape, size and location.
However,
it
is a computationally expensive method and served better
for
visualization.
Clinical MRIs are, however, mostly 2D due to the high cost and acquisition time
involved. Michopoulou et al. [
67
] presented the classi
cation of the Intervertebral
Discs (IVDs) into normal or degenerated, by using fuzzy C-Means to perform semi-
automatic atlas-based disc segmentation and then used a Bayesian classi
er. They
achieved 86
88 % accuracy on 34 cases. They also reported 94 % accuracy using
texture features [
66
] for 50 manually segmented discs.
A reasonable amount of research involving the use of real clinical MRIs on large
dataset from the same research group [
3
,
4
,
31
,
30
,
55
] and diagnostic reports has
also been reported. Alomari et al. [
4
] presented a fully automated herniation
detection system using GVF-snake for an initial disc contour and then trained a
Bayesian classi
-
er on the resulting shape features. They achieved 92.5 % accuracy
on 65 clinical MRI cases but a low sensitivity of 86.4 %. Alomari et al. [
3
] also
presented a desiccation diagnosis system in lumbar discs from clinical MRI using a
probabilistic model and achieving over 96 % accuracy. Ghosh et al. [
31
,
30
] pre-
sented a comprehensive comparison of features, dimensionality reduction tech-
niques and classi
city and
sensitivity. They were however evaluated on only 35 clinical cases. Koh et al. [
55
]
developed a computer-aided diagnosis framework for lumbar spine with a two-level
classi
ers for herniation detection resulting in high speci
ers. They used clinical MR image
data from 70 subjects in T1 and T2-weighted sagittal view for evaluation of the
system achieving 99 % herniation detection accuracy along with a speedup factor of
30 times in comparison with radiologist
cation scheme using heterogeneous classi
'
s diagnosis.
J
รค
ger et al. [
45
] presented a complete system for computer-aided assessment of
anomalies in 3-D MRI images of Scoliotic spine which provided an orthogonal
