Information Technology Reference
In-Depth Information
In vertebral fracture detection, Ghosh et al. [
29
] developed an unsupervised and
non-parametric approach for vertebral segmentation using Hough lines and mor-
phological operations. They also proposed a set of clinically motivated features
including vertebral height features for automatic fracture detection using a Support
Vector Machine (SVM). On 50 clinical cases they showed a segmentation error of
1.5 mm and a wedge fracture detection accuracy of 97 %. More recently, Al-helo
et al. [
2
] proposed another method using ASM and a GVF-snake for vertebra
segmentation and clinically motivated features for wedge fracture detection
resulting in 98 % accuracy (speci
city of 87.5 % and sensitivity over 99 %) using
an unsupervised learner.
5.3 Magnetic Resonance Imaging (MRI)
While CT proved to have higher sensitivity and speci
city in the visualization of
the bone structures, MRI provides superior contrast in visualizing the soft tissue that
surrounds the vertebrae, without ionizing radiation associated with CT or X-ray
imaging. Moreover, MRI does not subject the patient for harmful radiations of the
X-ray radiography and CT. It is important to highlight that research efforts in the
literature have not been focused on distinct problems. There are many overlaps in
research papers that may target localization, labeling, segmentation, and even
diagnosis. We provide approximate categorization below for the literature based on
the target problem.
5.3.1 Localization and Labeling
As early as 1989, Chwialkowski et al. [
19
] studied the localization of discs, ver-
tebrae and spinal cord in one MRI case using intensity pro
les and edge detectors.
A decade later, Booth et al. [
10
] used an algorithm based on symmetry, active
contours and edge detection to identify the vertebral body edges from cross-sec-
tional vertebral MRI. However, the unavailability of data prevented these efforts
from robust validation. Later in the last decade, Vrtovec et al. [
97
] detected the
spine curve from MRI using a polynomial model to provide a Curved Planar
Reformation (CPR) of the 3D spinal column. Their optimization framework is
based on the automatic image analysis of MR spine images that exploits some basic
anatomical properties of the spine. They tested the method on 21 axial MR scans of
the spine from twelve subjects, achieving mean errors of 2.5 mm and 1.7
°
for the
position of the 3D spine curve and axial rotation of vertebrae, respectively.
Mid last decade, Peng et al. [
78
] performed vertebra and disc labeling on
ve
whole spine MRIs, by extracting intensity pro
les of discs and use a convolution
operation to match a template of the disc. Later, Masaki et al. [
63
] proposed a
method for automated geometry planning based on intensity and a Hough transform
to localize the spine and the discs. They only used ten MRI normal cases for
