Computer Aided Diagnosis System for
Lumbar Spinal Stenosis
Using X-ray Images
Soontharee Koompairojn
Kien A. Hua
School of EECS
University of Central Florida
Chutima Bhadrakom
Department of Radiology
Thai Nakarin Hospital
Thailand
1
Outline
Background
Methodology
Classifiers Construction
 Automatic diagnosis

Prototype
Experimental Studies
Conclusions
2
Our Back
Spine is made up of a series of vertebrae (bone)
and disks (elastic tissue)
Spine
3
Facet Joints
• A joint is where two or more
bones are joined
• Joints allow motion
• The joins in the spine are
called Facet Joints
• Each vertebra has two set of
facet joints. One pair faces
upward and one downward
• Facet joints are hinge-like and
link vertebrae together
4
Spine Anatomy
First three sections of
the spine:
 Cervical Spine: Neck – C1
through C7
 Thoracic Spine: Upper and
mid back – T1 through T12
 Lumbar Spine: Lower back -
L1 through L5
5
Spinal Cord
 Each vertebra has a hole through it
 These holes line up to form the spinal canal
 A large bundle of nerves called the spinal cord runs
through the spinal canal
Jelly-like
nucleus
Hole
Holes
line up
Tough
outer
shell
6
Spinal Nerves
 Spinal cord has 31 segments;
and a pair of spinal nerves
exits from each segment
 These nerves carry messages
between the brain and the
various parts of the body
7
Link between Brain & Body
Each segment of
the spinal cord
controls different
parts of the body
8
Spinal Cord is Shorter
 Spinal cord is much shorter
than the length of the spinal
column
 Spinal cord extends down to
only the last of the thoracic
vertebrae
 Nerves that branch from the
spinal cord from the lumbar
level must run in the vertebral
canal for a distance before
they exit the vertebral column
9
Shape & Size of
Spinal Segments
 Nerve cell bodies are located in the “gray” matter
 Axons of the spinal cord are located in the “white”
matter. They carry messages.
 Spinal segments closer to the brain have larger
amount of “white” matter

Because many axons go up to the brain from all levels of
the spinal cord
More “white”
matter
10
Spinal Stenosis
 Spinal stenosis is a progressive narrowing of
the opening in the spinal canal, which places
pressure on the spinal cord (nerve roots)
 Pressure on nerve roots causes
 chronic pain, and
 loss of control over
some functions because
communication with the
brain is interrupted
11
Spinal Stenosis
 Cervical spinal stenosis: Stenosis (narrowing)
is located in the neck
 Lumbar Spinal Stenosis: Stenosis is located
on the lower part of the spinal cord
 75% of cases of spinal stenosis occur in the
low back (lumbar spine), and legs are affected

Produce pain in the legs with walking, and the pain
is relieved with sitting
12
We focus on
Lumbar Spine Stenosis
13
Diagnosis
 Patients with lumbar spinal stenosis may feel
pain, weekness, or numbness in the legs,
calves or buttocks
 Other conditions can cause similar symptoms
 Spinal tumors
 Disorders of the blood flow (circulatory disorders)
 Spinal stenosis diagnosis is not easy
14
We Try to Detect These
Conditions
 Disc Space Narrowing
 Abnormal Bony Growth (Posterior
osteophytes)
 Abnormality of FacetJoint (Posterior
Apophyseal Arthropathy)
 Vertibral Slippage (Spondylolisthesis)
15
Disc Space Narrowing
 As the spine gets older, the discs
lose height as the materials in
them dries out and shrinks
 Causing the middle part of
vertebrae to push down resulting
in bulging discs and herinated
discs
 Bulging discs and herinated discs
encroach into the canal to narrow
it and hence producing stenosis
16
Posterior Apophyseal Arthropathy
(abnormality of facet joint)
 Disc space narrowing can also
cause instability between
vertebrae
 The body attempts to reduce
the instability by trying to fuse
around the bad disc
 The facet joints enlarge and
the edges try to fuse together
and hence producing stenosis
17
Osteophytes
(abnormal bony outgrowth)
 Osteophyte - Small
abnormal bony outgrowth
(bone spurs)
 Anterior Osteophyte -
Outgrowth at the front
side of a vertebrae
 Posterior Osteophyte -
Outgrowth in the back
side of a vertebrae
18
Spondylolisthesis
A Vertebra is slipping
off another
19
Summary
 Disc Space Narrowing – bulging and herinated
discs
 Posterior osteophytes – bone spurs
 Posterior Apophyseal Arthropathy – abnormal
growth on facet joints
 Spondylolisthesis – vertebral slippage
We detect these conditions using X ray
20
Motivation
 Prior studies need manually determined
boundary for each individual vertebra
 No computer-aided diagnosis (CAD) system
for spinal stenosis
 Develop a fully automatic CAD for spinal
stenosis
 Focus on X-rays as this is often the first
test for spinal stenosis diagnosis
21
Imaging Technology
1.
X-RAYS: These show (1) disc narrowing, (2) bone
spurs (osteophytes), and (3) vertebrae slipping off
another (spondylo-listhesis)
2. CAT SCAN: This is a computerized X ray that
shows how much the diameter of the canal is
reduced and how far out the discs are
3. M.R.I. (Magnetic Resonance Imaging): It produces
picture like the CAT scan but they are generated
using a magnetic field (instead of radiation) – not
needed if the CAT scan shows the problems.
22
Features
I
I,J: Anteroposterior (A-P) width
of unusual spinal canal
D,E,F: Intervertebral
disc space height
D
E
J
F
H
A
B
G,H: Anteroposterior (A-P)
width of usual spinal canal
C
G
C: Posterior
vertebral height
A: Anterior
vertebral
height
B: Mid
vertebral
height
23
Feature Extraction
 Automatically determine
the boundary points

Using the Active
Appearance Model (AAM)
technique
 Measure the distances
among the boundary
points to extract the
features
Boundary point
25
Active Appearance Model
(morphable model)
 An AAM contains a statistical model of the
appearance of the object of interest (e.g., face)
which can generalize to almost any valid example
 The AAM can search for the structures from a
displaced initial position
Initial position
After 1 iteration
After 2 iteration
Convergence
Face model
Built from
400 images
26
Apply AAM to our Environment
1. A radiologist manually labels boundary
points of training images
2. Apply the AAM technique to build a lumbar
model (with boundary points)
3. Apply the lumbar model to determine the
boundary points of the image under
investigation
4. Measure the distances among the boundary
points to obtain the feature values
27
Spine X-ray image
28
Result from AAM
posterior osteophyte
(bone spur)
apophyseal arthopathy
(growth on facet joint)
spondylolisthesis
(vertebral slippage)
29
Predicting spinal conditions
• Bayesian framework is used to build a classifier
for each spinal condition
• Choosing the most probable spinal condition given
extracted features
# conditions
P*  Max p(Ci | x1 ,..., xd )
i 1
xi : Extracted features
Ci : Spinal condition i
P : Posterior probability for each spinal condition
P* : Highest posterior probability
If P* > threshold  spinal stenosis
Naïve Bayes Classifier (1)
• Prior Probability: Prior probabilities are
based on previous experience
Prior probabilit y for GREEN 
Prior probabilit y for RED 
Number of Green objects 40

Total number of objects
60
Number of Red objects 20

Total number of objects 60
31
Naïve Bayes Classifier (2)
• Likelihood: Likelyhood of X given Red/Green
X
Likelihood of X given GREEN 
Likelihood of X given RED 
Number of GREEN in the vicinity of X
1

Total number of GREEN cases
40
Number of RED in the vicinity of X
3

Total number of RED cases
20
32
Naïve Bayes Classifier (3)
Posterior Probability: combining the prior
and the likelihood to form a posterior
probability using Bayes’ rule
Posterior probabilit y of X being GREEN 
Prior probabilit y of GREEN  Likelihood of X given GREEN
Percentage of
Green population
Percentage of Green in
the neighborhood
X
33
Naïve Bayes Classifier (4)
Posterior probabilit y of X being GREEN 
4 1
1
Prior probabilit y of GREEN  Likelihood of X given GREEN  

6 40 60
Posterior probabilit y of X being RED 
Prior probabilit y of RED  Likelihoo d of X given RED 
2 3
1


6 20 20
We classify X as RED
34
Multiple Independent Variables
• Posterior probability for the event Cj among a set
of possible outcomes C = {C1, C2, …, Cd)

   x , x ,..., x | C 
p C j | xi , x2 ,..., xd  p C j  p
i
2
d
j
Posterior probability of class membership, i.e.,
the probability that X belongs to Cj

  
d
p C j | xi , x2 ,..., xd  p C j   p
Conditional probability of independent
Variables are statistically independent
k 1
Likelihood
x | C 
k
j
Likelihood
35
Multiple Independent Variables
• Probability that X belongs to Cj

  
d
p C j | xi , x2 ,..., xd  p C j   p
k 1
x | C 
k
j
• Using Bayes’ rule above, we label a new case
X with a class level Cm that achieves the
highest posterior probability
p (C m | X )  Max  p (C i | X ) 
#classes
i 1
 X belongs to Cm
36
Automatic Stenosis Diagnosis
• Probability that X belongs to Cj

  
d
p C j | xi , x2 ,..., xd  p C j   p
k 1
x | C 
k
j
• Using Bayes’ rule above, we diagnose a new
case X as follows:
p (C m | X )  Max  p (C i | X ) 
#conditions
i 1
If p(Cm|X) > threshold  spinal stenosis
37
System Architecture
New X-ray case
Training interface
User interface
Training &
learning process
Image
segmentation
Result
X-ray
training cases
Feature
Extraction
Feature
Extraction
Classifier
Classification
Automatic diagnosis
Feature Vectors
Classifiers construction
38
GUI for Classifier Construction
The user interface for managing training images and
building lumbar spine classifiers
39
GUI for Stenosis Diagnosis
The user interface for submitting X-ray images
for analysis of spinal conditions
40
Data Set for Experiments
86 lumbar spine X-ray images from NHANES II
database
70 cases for training
16 cases for testing
Spinal Conditions
Intervertebral Disc Level
L2-L3 L3-L4 L4-L5 Total
Posterior Osteophyte
5
2
4
11
Posterior Apophyseal Arthorphathy
7
13
20
40
Disc Space Narrowing
30
33
35
98
Spondylooisthesis
1
0
1
2
Spinal Stenosis
12
15
24
51
There are 17,000 spine X-ray images in the NHANES II database
collected by the second National Health and Nutrition Examination Survey
41
Average Percentage of correct
prediction of training images
Spinal Conditions
Intervertebral Disc Level
L2-L3
L3-L4
L4-L5
Total
Posterior Osteophyte
100.0
98.6
100.0
99.5
Posterior Apophyseal Arthorphathy
97.1
82.9
80.0
86.7
Disc Space Narrowing
84.3
87.1
80.0
83.8
Spondylooisthesis
100.0
100.0
100.0
100.0
Spinal Stenosis
100.0
95.7
97.1
97.6
42
Average Percentage of Correct
Prediction of test images
Spinal Conditions
Intervertebral Disc Level
L2-L3
L3-L4
L4-L5
Total
Posterior Osteophyte
87.5
100.0
92.0
93.2
Posterior Apophyseal Arthorphathy
90.6
81.3
78.0
83.3
Disc Space Narrowing
68.8
68.8
50.0
62.5
Spondylooisthesis
100.0
100.0
92.0
97.3
Spinal Stenosis
79.7
75.0
68.8
74.5
43
Average Percentage of correct
prediction using perfect labels
Spinal Conditions
Intervertebral Disc Level
L2-L3
L3-L4
L4-L5
Total
Posterior Osteophyte
100.0
100.6
87.5
95.8
Posterior Apophyseal Arthorphathy
81.3
87.5
81.3
83.4
Disc Space Narrowing
81.3
81.3
62.5
75.0
Spondylooisthesis
100.0
100.0
93.8
97.9
Spinal Stenosis
93.8
87.5
75.0
85.4
Better labeling improves performance
44
Conclusions
A fully automatic CAD system for lumbar spinal
stenosis
Not dependent on user’s knowledge and
experience
Accuracy from 75 – 80%
Good enough for screening and initial diagnosis
Suitable for general practitioners
45
Do You Know ?
 Giraffes and human have SEVEN
vertebrae in their necks
46