medical image segmentation using hidden markov random field a distributed approach
TRANSCRIPT
The Third International Conference on Digital Information Processing and Communications (ICDIPC 2013)
Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach
Theme
SAMY AIT-AOUDIA,
RAMDANE MAHIOU,
EL-HACHEMI GUERROUT
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
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One exam by CT (Computed Tomography) scanner can produce hundred images.
All of these images represents a 3D
image
Processing and analysis of these images becomes a difficult and daunting task
The classical analysis of medical cuts
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I N T R O D U C T I O N
Problem
3D automatic segmentation
The 3D image The segmented 3D image
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I N T R O D U C T I O NSolution :
Tool to aid the physician to make the decisionbased on Automatic segmentation.
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T H E A I MRelevance of the physician aid tool to
make the decision based on
OUR AIM
The time of computation The quality of segmentation
TIME + QUALITY
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
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S E G M E N T A T I O N B Y U S I N G H M R F
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1 23 4
Y: Observed Image
X: Hidden Image
2C,s
2s ),(2-(1)2ln(2
)²-(yy)(x,
ttsx
Ss x
x xxTs
s
s
y)(x,minarg Xx
x
S E G M E N T A T I O N B Y U S I N G H M R F
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Optimizations techniques are used like ICM, …
Problem
Minimizing the function (x,y) is computationally intractable.
Solution
S E G M E N T A T I O N B Y U S I N G H M R FICM Algorithm:
1. Initialization: Start with an arbitrary labeling x0 and let n=0.
2. At step n:
Visit all the sites according to a visiting scheme and in every site :
,
3. Increment n. Goto 2, until a stopping criterion is satisfied.
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( )
1 arg min ( )card S
ns s s
xx U x
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
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E X P E R I M E N T A L R E S U LT S
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Configuration Hardware :The cluster of eight identical machines Switch (Catalyst 3560G)
Configuration Software:The Parallelization library is Open MPIPlatform application framework Qt Linux system (ubuntu 11.04)
E X P E R I M E N T A L R E S U LT S
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Benchmark Name of benchmark Dimension Link
1 MRI Phantom 8Bits(t1_icbm_normal_1mm_pn
0_rf0.rawb)181 x 217 x 181
http://mouldy.bic.mni.mcgill.c
a/brainweb/anatomic_normal.html
2 Head MRT Angiography 8Bits
(mrt8_angio2.raw)256 x 320 x 128
http://www.gris.uni-tuebingen.de/edu/areas/scivis/volren/datasets/
new.html
3 Head MRI CISS 8Bits (mri_ventricles.raw) 256 x 256 x 124
http://www.gris.uni-tuebingen.de/edu/areas/scivis/volren/datasets/
new.html
Benchmarks images used in our tests.
E X P E R I M E N T A L R E S U LT S
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Visual results Benchmark : 1
E X P E R I M E N T A L R E S U LT S
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Visual resultsBenchmark : 2
E X P E R I M E N T A L R E S U LT S
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Visual resultsBenchmark : 3
Evaluating the quality of the segmentation
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FNFPTP2TP2DC
Kappa index
Ground truth
The image to segment
The segmented image
E X P E R I M E N T A L R E S U LT S
E X P E R I M E N T A L R E S U LT SComparison : Mean kappa index values Benchmark : 1Slices : 90-119 Methods : Otsu, MoG, MoGG and our method
17White Mat -
terGray Matter CSF Matter
00.10.20.30.40.50.60.70.80.9
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OtsuMoGMoGGOur Method
Methods
Kappa Index
Speed-up
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E X P E R I M E N T A L R E S U LT S
Processing Time
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E X P E R I M E N T A L R E S U LT SProcessing Time
1 PC 2 PCs 4 PCs 8 PCs0
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Benchmark 1Benchmark 2Benchmark 3
Time (h)
Number of PCs
Benchmarks
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E X P E R I M E N T A L R E S U LT SSPEED UP
1 PC 2 PCs 4 PCs 8 PCs0
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Benchmark 1Benchmark 2Benchmark 3
Speed-up
Number of PCs
Benchmarks
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
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The kappa index can be used only when we know beforehand segmentation ground truth .
In our tests we notice our implemented method seems generally better than the thresholding-based segmentation methods (Otsu, MoG, MoGG ).
The processing time is improved by the use of a cluster of PCs.
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C O N C L U S I O N A N D P E R S P E C T I V E S
However, further work must take into account like :
The cluster of PCs must be incremented to see the limits of its contribution.
Comparison with other methods
Implementation of other optimization methods
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C O N C L U S I O N A N D P E R S P E C T I V E S
Thank you for your attention