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meta-data of the patient such as weight, height, and history. Patient
'
s low back
structures vary based on their weight, height, and history.
While most of the literature in localization provides disc centroids [
5
,
9
,
76
,
82
].
Ghosh et al. [
32
] presented an approach using heuristics and machine learning
methods to provide tight bounding boxes for each disc achieving 99 % localization
accuracy on 53 cases. This method can by-pass complicated segmentation algo-
rithms and directly feed the detected disc region to a CAD system that extracts
relevant features and automatically provides diagnostic results [
30
,
31
].
5.3.2 Segmentation
Few research efforts have been conducted on segmentation of vertebrae from MRI
despite that bones are better outlined in CT scans. In 2004, Carballido-Gamio et al.
[
13
] discussed the segmentation of vertebral bodies from sagittal T1-Weighted
(T1 W) MRI using normalized cuts [
85
] with Nystr
รถ
m approximation method [
25
].
T1 W MRI were
first preprocessed by Anisotropic Diffusion algorithm [
79
] that
smooths the image without distorting the edges. However, they test their work on
only six subjects for lumbar area. Five years later, Huang et al. [
43
] proposed a
statistical learning approach based on an improved AdaBoost algorithm for ef
cient
vertebra detection from MRI with a success of 98 % on less than 25 cases.
As early as 1997, Roberts et al. [
81
] proposed a method based on watershed
algorithm to segment the
five lumbar level discs from MRI. However, they required
major user intervention by carefully selecting an ROI. Their work studies the
relation between patient age and disc height. They concluded that the disc height
increases with aging and that it increases from L1-L2 level and decreases at L5-S
level. Later on, Hoad and Martel [
40
] presented a technique to segment the bone
and soft tissues from MRI. However, their method requires sensitive initialization
by the user to locate four points on each vertebrae. Wachter et al. [
99
] used various
image segmentation techniques including shape model, Hough transforms, and
edge detectors to segment the 3D spine and discs in the cervical area from full 3D
MRI. They did not report the number of validation cases except stating that they are
several T1 W and T2 W cases. Couple years later, Chevre
ls et al. [
17
] proposed a
method to segment the discs based on Watershed and many image processing
techniques including opening and erosion. This method, however, encounters an
over-segmentation issue. To overcome this problem, the same group [
18
] also
presented a framework for automatic segmentation of intervertebral discs of Sco-
liotic spines from 2D and 3D MRI. Twenty two texture features (18 statistical and
four spectral) were extracted from every closed region obtained from their earlier
segmentation procedure [
17
]; followed by PCA and clustering which resulted in an
overall accuracy of 85 %, speci
city of 83 % and sensitivity of 87 % on 505 images
derived from only three patients.
A Hough transform based approach was presented by Shi et al. [
86
] which
showed success on 48 out of 50 cases but no quantitative evaluation was discussed.
Moreover, the
