This document discusses using NVIDIA TK1/TX1 platforms for medical image processing. It summarizes segmenting brain MRI images using fuzzy c-means clustering on these platforms. Performance is evaluated for single and multiple GPU implementations using different precision formats and memory copy modes. Texture-based image processing for mammographic images is also discussed.

1. TAIPEI | SEP. 21-22, 2016
Che-Lun Hung, 2016/9/21
MEDICAL IMAGE PROCESSING ON
NVIDIA TK1/TX1

2. 2
AGENDA
Medical Image
Brain MRI Image Segmentation
Fuzzy C-Means Method on TK1/TX1
Genetic Fuzzy C-Means Method on Multiple TK1s

3. 3
MEDICAL IMAGE

4. 4
MEDICAL IMAGE
X-Ray
Ultrasound
Image resource : wiki
CT MRI
Image Source:
https://en.wikipedia.org/

5. 5
WHY NEED MEDICAL IMAGE PROCESSING?
9/26/16
Computer-aided diagnosis Description of Lension
Image Source:
http://www.dailyherald.com/
Image Source:
http://artificialintelligencefordummies.weebly.com/

6. 6
TK1/TX1
TK1
Kepler
192 CUDA cores
2G Memory
TX1
Maxwell
256 CUDA cores
4G Memory

7. 7
BRAIN MRI IMAGE SEGMENTATION

8. 8
BRAIN MRI IMAGE
Image Source: http://brainweb.bic.mni.mcgill.ca/brainweb/

9. 9
SEGMENTATION
Image Source: http://brainweb.bic.mni.mcgill.ca/brainweb/

10. 10
CLUSTERING METHODOLOGY

11. 11
SEGMENTATION FOR BRAIN MRI

12. 12
FUZZY C-MEANS METHOD ON TK1/TX1

13. 13
FUZZY C-MEANS METHOD
Jm = uij
n
d xi,θj( )
j=1
c
∑
i=1
n
∑

14. 14
EXPERIMENTAL PLATFORMS
TK1/TX1
The host (CPU) Intel Xeon E3-1231 v3 3.40GHz with 64GB RAM
Data Size 1150*1280
9/26/16

15. 15
SEGMENTATION RESULTS
Image Source: http://brainweb.bic.mni.mcgill.ca/brainweb/

16. 16
PERFORMANCE
1
7.2
12.9
0
2
4
6
8
10
12
14
Intel E3 CPU TK1 GPU TX1
Speedup
Platform
IMG Size= 1150*1280

17. 17
DIFFERENT PRECISION FORMAT
5.93
2.22
3.81
1.42
0
1
2
3
4
5
6
7
TK1 GPU double TK1 GPU float TX1 GPU double TX1 GPU float
Time(seconds)
Platform

18. 18
TK1/TX1 MAXIMUM PERFORMANCE MODE
5.93
2.22
3.81
1.42
5.45
2.15
3.69
1.39
0
1
2
3
4
5
6
7
TK1 GPU double TK1 GPU float TX1 GPU double TX1 GPU float
Time(seconds)
Platform
Kernel time(Normal) Kernel time(HP)

19. 19
DIFFERENT MEMORY COPY MODES
2.15
1.39
2.38
1.5
2.37
1.45
0
0.5
1
1.5
2
2.5
TK1 GPU float TX1 GPU float
Time(s)
Platform
Kernel time Kernel time(Zero-copy) Kernel time(Unified memory)

20. 20
GENETIC FUZZY C-MEANS METHOD ON
MULTIPLE TK1S

21. 21
GENETIC FUZZY C-MEANS METHOD
Image Source: http://http://www.turingfinance.com/

22. 22
MESSAGE PASSING INTERFACE (MPI)
Image Source: https://computing.llnl.gov/tutorials/mpi/

23. 23
MULTIPLE GPU
MPI+CUDA

24. 24
EXPERIMENTAL PLATFORMS
TK1/TX1
The hosts (CPU) Intel Xeon E5-2620 2.5GHz, E3-1231 V3 3.4GHz, and I7-4280K 3.7GHz.
Data Size 1150*1280
9/26/16

25. 25
DIFFERENT POPULATION SIZES
(a) (b)
(c) (d)
(e) (f)
(a) population size = 30,
(b) population size = 60,
(c) population size = 90,
(d) population size = 120,
(e) population size = 150,
(f) population size = 180.
Image Source: http://brainweb.bic.mni.mcgill.ca/brainweb/

26. 26
PERFORMANCE

27. 27
SUITABLE FOR ALL MEDICAL IMAGE
PROCESSING TECHNOLOGIES?

28. 28
MAMMOGRAPHIC IMAGE

29. 29
TEXTURE-BASED IMAGE PROCESSING (1)

30. 30
TEXTURE-BASED IMAGE PROCESSING (2)
127*127127*127127*127127*127 ……………127*127 127 * 127 threads

31. 31
EXPERIMENTAL PLATFORMS
TK1/TX1
The host (CPU) Intel Xeon E3-1231 v3 3.40GHz with 64GB RAM
Data Size 1123*1751
9/26/16

32. 32
PERFORMANCE
0
5000
10000
15000
20000
25000
30000
35000
Matlab with NVIDIA
GTX 980
Matlab with Intel Xeon
E3-1231
NVIDIA TX1 NVIDIA TK1
550 1752
20260
31469
Executiontime(s)
Device

33. 33
MORE POSSIBILITIES FOR GPU ON MEDICAL
IMAGE PROCESSING

34. TAIPEI | SEP. 21-22, 2016
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