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)

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Original Image Histogram

Grey Scale Value0 50 100 150 200 250

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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

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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?