Information Technology Reference
In-Depth Information
5.1 X-ray Radiography
Since X-ray radiographs use ionizing radiation and show better detailing of the
bony tissues, there has been plenty of research in the direction of vertebrae seg-
mentation from X-ray scans. Moreover, the availability of X-ray radiographs data
helped boost the related amount of research.
5.1.1 Vertebrae
More than two decades ago, Hedlund and Gallagher [
39
] performed vertebral
morphometry on lateral thoracic and lumbar X-ray radiographs of 153 women with
a preliminary diagnosis of Spinal Osteoporosis. Measurements included anterior
and posterior vertebral height, width, area, wedge angle, percent reduction of
Anterior to Posterior Height (PRH) and Percent Difference in Anterior Height
between adjoining vertebrae (PDAH). They showed that among individuals with
mild Osteoporosis (0
2 fractures) PDAH identi
ed 86 % of the fractures and 95 %
-
of the individuals with fractures.
Manual selection of anatomical points for vertebral abnormality diagnosis is
time consuming, imprecise and subjective. To obtain a more objective and accurate
description of the vertebral body shape, semi-automatic methods were proposed
that were based on statistical models of vertebral bodies in the sagittal view. Very
early on, in 1993, a computerized quantifying technique for vertebral morphometry
on lateral radiographs of the spine was proposed by Nicholson et al. [
73
]. Although
fracture detection was improved by expanding the description of the vertebral body
shape from six points to a contour, the amount of traumatic spinal injury or latent
vertebral fracture was often underestimated. The main reason for the wrong diag-
nosis originated from the limited measurement possibility caused by the lack of
depth perception in X-ray radiographs. Later in 1997, Smyth et al. [
87
] described
how Active Shape Models (ASM) could be used to locate both normal and frac-
tured vertebrae from Dual energy X-ray Absoptiometry (DXA) images of the spine.
However, three initialization points have to be manually selected. To overcome the
lack of depth perception in X-ray images, Benameur et al. [
8
] performed projection
of a three-dimensional (3D) statistical shape model of a vertebra to a pair of
orthogonal 2D X-ray radiographs. They validated this method on 57 scoliotic
vertebrae images. However, the proposed segmentation was highly dependent on
model initialization.
Various efforts that target the diagnosis of certain vertebra conditions involved
localization and segmentation. In 2000, Long and Thoma [
60
] investigated the
segmentation of C2 and C3 vertebrae from the cervical area using an ASM as a
rst
step for building an image based retrieval system for a dataset consisting of
7,000 lumbar X-ray radiographs and 10,000 cervical spine X-ray radiographs. They
built the Web-based Medical Information Retrieval System (WebMIRS) based on
the National Health and Nutrition Examination Surveys (NHANES). Later,
