Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach
-
Upload
el-hachemi-guerrout -
Category
Health & Medicine
-
view
354 -
download
3
Transcript of Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach
![Page 1: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/1.jpg)
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
![Page 2: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/2.jpg)
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
2
![Page 3: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/3.jpg)
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
3
I N T R O D U C T I O N
Problem
![Page 4: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/4.jpg)
3D automatic segmentation
The 3D image The segmented 3D image
4
I N T R O D U C T I O NSolution :
Tool to aid the physician to make the decisionbased on Automatic segmentation.
![Page 5: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/5.jpg)
5
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
![Page 6: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/6.jpg)
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
6
![Page 7: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/7.jpg)
S E G M E N T A T I O N B Y U S I N G H M R F
7
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
![Page 8: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/8.jpg)
S E G M E N T A T I O N B Y U S I N G H M R F
8
Optimizations techniques are used like ICM, …
Problem
Minimizing the function (x,y) is computationally intractable.
Solution
![Page 9: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/9.jpg)
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.
9
( )
1 arg min ( )card S
ns s s
xx U x
![Page 10: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/10.jpg)
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
10
![Page 11: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/11.jpg)
E X P E R I M E N T A L R E S U LT S
11
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)
![Page 12: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/12.jpg)
E X P E R I M E N T A L R E S U LT S
12
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.
![Page 13: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/13.jpg)
E X P E R I M E N T A L R E S U LT S
13
Visual results Benchmark : 1
![Page 14: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/14.jpg)
E X P E R I M E N T A L R E S U LT S
14
Visual resultsBenchmark : 2
![Page 15: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/15.jpg)
E X P E R I M E N T A L R E S U LT S
15
Visual resultsBenchmark : 3
![Page 16: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/16.jpg)
Evaluating the quality of the segmentation
16
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
![Page 17: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/17.jpg)
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
1
OtsuMoGMoGGOur Method
Methods
Kappa Index
![Page 18: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/18.jpg)
Speed-up
18
E X P E R I M E N T A L R E S U LT S
Processing Time
![Page 19: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/19.jpg)
19
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
1
2
3
4
5
6
7
8
9
Benchmark 1Benchmark 2Benchmark 3
Time (h)
Number of PCs
Benchmarks
![Page 20: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/20.jpg)
20
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
1
2
3
4
5
6
7
8
9
Benchmark 1Benchmark 2Benchmark 3
Speed-up
Number of PCs
Benchmarks
![Page 21: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/21.jpg)
INTRODUCTION
SEGMENTATION BY USING HMRF
EXPERIMENTAL RESULTS
CONCLUSION AND PERSPECTIVES
P L A N
21
![Page 22: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/22.jpg)
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.
22
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
![Page 23: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/23.jpg)
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
23
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
![Page 24: Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach](https://reader033.fdocuments.in/reader033/viewer/2022052606/587712b81a28ab4c1d8b4863/html5/thumbnails/24.jpg)
Thank you for your attention