MRI Image Segmentation
for Brain Injury
Quantification
Lindsay Kulkin
BRITE REU 2009
Advisor: Bir Bhanu
August 20, 2009
Overview




Background
◦ Stroke Diagnosis
◦ Forms of Image
Segmentation
Process
◦ Gradient Relaxation
Algorithm
◦ Connected Components
◦ K-Means Clustering Algorithm
Results
Conclusions
◦ Other ways to apply these
forms of analysis
Background




What is a stroke?
Types
 Ischemic
 Hemorrhagic
Causes
 Thrombosis*
 Embolism
 Systemic Hypoperfusion
Diagnosis
 Computed Tomography
(CT) scan
 Magnetic Resonance
Imaging (MRI)
*Thrombosis occurs when a blood
clot (known as a thrombus) forms
within the blood vessel and does
not break free.
Image Segmentation
Manual Segmentation
Automatic Segmentation
• Time consuming and often
inaccurate
• Can vary over 30% from
person to person and can take
hours per patient
• A faster and more accurate
process
• Repeatable and would take
a matter of minutes
Original Image
Manual
Segmentation
Automatic
Segmentation
Gradient Relaxation Algorithm
Original Image Histogram
1000
900
800
Pixel Value
Find the initial assignment of
probability (Pi) and the mean
neighborhood probability (qi)
700
600
500
400
300
Find the maximum kept constant
(ρimax) and the ρi constant for all
pixels
200
100
0
Gray Scale Value
0
50
1000
100
150
200
Grey Scale Value
250
First Iteration Histogram*
900
800
Pixel Value
Construct a threshold image*
700
600
500
400
Based on the valley of the
histogram, segment the first
iteration and create a binary image
(threshold value = 130)
300
200
100
0
0
Grey Scale Value
50
100
150
Grey Scale Value
200
250
Gradient Relaxation Algorithm
Original Image
First Iteration
Binary Image
Images provided by the Loma Linda University Medical Center, 2007
• With each iteration,
each new pixel value is
determined based on
the probability of its
own pixel value as well
its neighboring pixels
(3x3 window)
• While the program
runs until it terminates,
the threshold is
automatically selected
based on the histogram
of the first iteration
Connected Components Analysis
Mask
Pixel labels for Binary Image
Preliminary Scan
1
1
0
2
2
2
0
3
1
1
0
2
0
2
0
3
1
1
1
1
0
0
0
3
0
0
0
0
0
0
0
3
4
4
4
4
0
5
0
3
0
0
0
4
0
5
0
3
6
6
0
4
0
0
0
3
6
6
0
4
0
3
3
3
Final Image
• Connected components
identifies contiguous sets of
connected pixels and is reapplied
until the image cannot be
segmented any further
Connected Components Analysis
Threshold Image
Inverted Image
Connected
Components
Total pixels excluding background: 11,610
White: 10,940 (94.2%)
Large Injury: 502 (4.32%)
Small Injury: 168 (1.45%)
K-Means Clustering Algorithm
Isolate each component by
setting all other pixels to zero
Select a k value as the initial
cluster centers and find the
distance between each pixel and
each cluster center
Find the mean value of each
cluster center
For all pixels, assign each pixel
to its closest cluster center. Find
the mean value of each cluster
center until the cluster centers
do not change
Original Image
K-Means Clustering Algorithm
Total pixels excluding
background: 502
Yellow: 272 (54.2%)
Aqua: 124 (24.7%)
Blue: 106 (21.2%)
Total pixels excluding
background: 10,653
Yellow: 602 (5.7%)
Red: 5740 (53.9%)
Blue: 4311 (40.5%)
Total pixels excluding
background: 168
Yellow: 89 (53%)
Aqua: 79 (47%)
Data Analysis
Gradient Relaxation
Algorithm
K-Means Clustering
Algorithm
Manual
Segmentation
Damaged Area
(Pixels)
Percent Damaged
Form of Analysis
Total Area
(Pixels)
Gradient Relaxation
11,610
670
5.77
K-Means Clustering
10,653
602
5.65
Manual
Segmentation
11,610
759
6.54
S.D.
Mean
0.48
5.99
Conclusions
•

•
Automatic segmentations vs. manual
segmentation
• Both are effective and consistent
• Automatic segmentation is much faster
These approaches can be applied to
each MRI slice and the volume of injury
can be obtained
In the future, other forms of brain injury
can be analyzed through the use of
either:
• The gradient relaxation
algorithm/connect components
analysis
• K-Means Clustering algorithm
Acknowledgments
I would like to thank:

Professor Bir Bhanu for his guidance

My graduate student advisor Benjamin X. Guan, as well
as Angello Pozo and Giovanni DeNina

The Center for Research in Intelligent Systems (CRIS)

Jun Wang for this opportunity and for his support

Loma Linda University Medical Center for providing the
MRI images
Questions?