Post on 20-Jan-2016
MRI Image MRI Image Segmentation Segmentation for Brain Injury for Brain Injury QuantificationQuantification
Lindsay KulkinBRITE REU 2009Advisor: Bir BhanuAugust 20, 2009
OverviewOverview 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
BackgroundBackground 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 SegmentationImage SegmentationManual 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 ImageManual
SegmentationAutomatic
Segmentation
Gradient Relaxation Gradient Relaxation AlgorithmAlgorithm
Find the maximum kept constant (ρimax) and the ρi constant for all
pixels
Find the initial assignment of probability (Pi) and the mean neighborhood probability (qi)
Construct a threshold image*
Based on the valley of the histogram, segment the first
iteration and create a binary image (threshold value = 130)
0
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Gray Scale Value
Pix
el V
alue
Original Image Histogram
Grey Scale Value0 50 100 150 200 250
0
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1000First Iteration Histogram*
Pix
el V
alue
Grey Scale Value
Grey Scale Value0 50 100 150 200 250
Gradient Relaxation Gradient Relaxation AlgorithmAlgorithm
Images provided by the Loma Linda University Medical Center, 2007
Original Image
First Iteration Binary Image
• 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 Connected Components AnalysisAnalysisMask
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
Pixel labels for Binary Image
Preliminary Scan
Final Image
• Connected components identifies contiguous sets of connected pixels and is reapplied until the image cannot be segmented any further
Connected Components Connected Components AnalysisAnalysis
ConnectedComponentsThreshold Image Inverted Image
Total pixels excluding background: 11,610White: 10,940 (94.2%)Large Injury: 502 (4.32%)Small Injury: 168 (1.45%)
K-Means Clustering K-Means Clustering AlgorithmAlgorithm
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 K-Means Clustering AlgorithmAlgorithm
Total pixels excluding background: 10,653
Yellow: 602 (5.7%)Red: 5740 (53.9%)Blue: 4311 (40.5%)
Total pixels excluding background: 502
Yellow: 272 (54.2%)
Aqua: 124 (24.7%)
Blue: 106 (21.2%)
Total pixels excluding background: 168
Yellow: 89 (53%)
Aqua: 79 (47%)
Data AnalysisData Analysis
Form of AnalysisTotal Area (Pixels)
Damaged Area (Pixels)
Percent Damaged
Gradient Relaxation 11,610 670 5.77
K-Means Clustering 10,653 602 5.65
Manual Segmentation 11,610 759 6.54
S.D. 0.48Mean 5.99
Gradient Relaxation Algorithm
Manual Segmentation
K-Means Clustering Algorithm
ConclusionsConclusions• 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
AcknowledgmentsAcknowledgments
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? Questions?