1. Near Real Time Early
Esophageal Cancer
Detection Using a
Graphics Processing
Unit
Jason Helms

2. Introduction
Purpose
Image Processing
Discrete Wavelet Transform
Fractal Dimension Decomposition
General Methodology
Phase Breakdown
Base Results
Threshold Identification
Final Results
Timing Results
Future Work
Conclusions
Agenda

3. Introduction
Jason Helms
BS in CS at EWU
Medical processing has been recurrence
“Grand Challenges in Biomedical Image Analysis”
“Sub-Challenge: Early Barrett’s cancer detection”

4. Purpose
Identify Lesions
Input: Endoscopic image
Current: One image at a time
Extended future goal: Video application
Why: Getting data expensive, reduce patient
burden, optimize input data gathering
Near Real Time
Saga University time: 3 minutes 0 seconds
Problems: Few unknowns in regards to
aspects of the paper.
Goal: < 10s/image
Why: 0:30 video @ 30 fps = 900 images
9000 s = 2.5 hours.

5. Purpose

6. [ 1 ]
Purpose

7. [ 2 ]
Purpose

8. [ 2 ]
Purpose
J. Yamaguchi, A. Yoneyama, and T. Minamoto, “Automatic detection of early
esophageal cancer from endoscope image using fractal dimension and discrete
wavelet transform,”
Original Work
Published 2015
Base method

9. Previous Team Goal
Time: 3:00 (m:ss) Time: < 10s [ 3 ]

10. Previous Team Goal
Time: 3:00 (m:ss) Time: < 10s [ 3 ][ 9 ]

11. Image
Processing
OpenCV
C++
QT Creator (phase management)

12. OpenCV
- GPU integration in 2011
- Version used: 3.0.0
- OpenCV Focus: Real Time
Image Processing
- Diverse Hardware Compatibility
[ 4 ]

13. Qt Creator: used to manage phases
C++: OpenCV, QT Creator
C++ & QT Creator
[ 5 ]

14. Used for:
- Input reduction
- Small Vein Filter
- Minimal loss of input details
The first DWT was invented by the
Hungarian mathematician Alfréd
Haar.
Discrete Wavelet
Transform

15. Fourier Transform
[ 8 ]
Good Frequency Resolution
No time resolution

16. Short Time Fourier Transform
[ 8 ]
Time windows
Big window -> good frequency resolution
Small window -> good time resolution

17. - For any pixel in {LL,LH,HL,HH}
at (i,j)
- Convolute that pixel with filters,
also called kernels, p and q
DWT

18. DWT
M = 1
p = {1}
q = {1}
Easy
Fast
Haar

19. Measures pattern complexity
Not a unique descriptor
One way processing
Box counting method
Fractal
Dimension

20. Fractal Dimension
[ 6 ]

21. Koch Example
[ 10 ]

22. First convert image to Binary using
Dynamic Thresholding.
Recursively cover image in grid of
boxes
Count boxes with a 1
Compute: log(# of boxes with one’
s) divided by log (length of box
side) and save in variable (DT)
Box Counting

23. Box Counting
Visual
Demonstration

24. Box Counting
Final Steps
After all boxes have been
processed the following calculated
for each color:
Call this D{C} where C is from {R,G,
B,L} then:
Total Box Result (BT) for a block is:
BT = DR * DB * DG * DL

25. Gaussian
Fuzzy Edge
Enhancement
Fast, thanks to OpenCV
Computes a fuzzy image from input
Performs a weighted addition of
input image and fuzzy image

26. General Methodology
Phase 1
Codename: clipping
Purpose: preprocessing
Difficulty: Eh?
Phase 2
Codename: pre-wavelet
Purpose: preprocessing
Difficulty: Easy
Phase 3
Codename: DWT
Purpose: final preprocessing
Difficulty: Medium
Phase 4
Codename: Box Count
Purpose: detect patterns
Difficult: Hard

27. Phase 1
RGB Decomp
Input is a PNG
3 Channel
RGB
Lum. Calc.
No standard
Y = a * R + b * G + c * B
Size Reduce
Input Dimensions: 1600 x 1200
Need: 1024 x 1024
3 channel -> single channel
Edge Enhance.
Gaussian Fuzzy Edge
Enhancement
Brightness Enhance

28. Sample input image, not test image
Red, Green, Blue and Luminance
decomposition
Next up

29. [ 7 ]

30. [ 7 ]

31. Before Edge Enhancement
Raw Color Physician Anno.

32. Before After
Results

33. Phase 2
Grid Reduction
Input: 1024x1024 image
Output: 256 images
Output image size: 128x128
Color Thresh
Eliminate blocks without
enough intensity detail

34. Grid decomposition
Sample pre-color intensity
Sample high color intensity
Next up

35. [ 7 ]

37. Color Intensity Thresh Too High
R B
G L

38. Phase 3
DWT-1
Input size: 128x128
Output size: 64x64
DWT-2
Input size: 64x64
Output size: 32x32

39. DWT reduction sample
Small Vein Filter - convert & magnify
Small Vein Filter - magnify and dwt 1
Small Vein Filter - dwt 2
Small Vein Filter - before after
Next up

40. Input Reduction

41. Small Vein Filter

42. Small Vein Filter

43. Small Vein Filter

44. Small Vein Filter

45. Input Reduction

46. Phase 4
Bin. Image
Input: single channel (grayscale)
image
Output: Binary image
Box Counting
Input: 16 x 16 pixel binary image
Output: float
Results?
Still need statistics, but we can
see something, too many to be
coincidence
High false positive rate
Grid Reduction
Input: 32 x 32
Output: four 16x16 pixel images
Non-overlapping

47. Phase 4 set up
Color Intensity to Binary
Box count output
Next up

50. Box Counting
Visual
Demonstration

52. Disappointing
One glimmer of hope
Threshold detection begins
Not final results - coming soon
Base Results

56. From one promising square to
identifying all of the suspected area
marked by physicians.
Begins with color intensity
thresholding
Threshold
Identification

57. Color Intensity Thresh Too High

58. Lower Color Intensity Thresh

59. Converting an image to binary
uncovered yet another threshold set
to a blind average: Dynamic
thresholding
Relaxed this threshold
Threshold
Identification

60. Threshold Identification
Too low Better

61. Identified another in DWT, left at
blind average
Did not make sense (visually) to
change
However, Daubechies with D=4
would be better but costly
Threshold
Identification

62. Following Results:
Gaussian Fuzzy Edge Enhancement
Color Intensity Thresh: 60%
Bin Thresh: 80%
RESULTS

63. 2 sets of results with all colors
shown
4 sets of results with the input,
physican annotation and final results
Next up

64. Patient 7 Image 1

67. Patient 10 Image 2

68. Patient 10 Image 2

69. Patient 10 Image 2

70. Previous Team Goal
Time: 3:00 (m:ss) Time: < 10s [ 3 ]

71. Patient 17 Image 5
Before Phy. Anno. Results

72. Patient 16 Image 3
Before Phy. Anno. Results

73. Patient 12 Image 3
Before Phy. Anno. Results

74. Patient 19 Image 1
Before Phy. Anno. Results

75. Patient 13 Image 2
Before Phy. Anno. Results

76. The bar wasn’t that high to begin
with anyways…
Obliterated Saga University Time
Beat the goal
Timing Results

77. Nvidia: ~1.35 seconds/image
AMD: ~3.5 seconds/image
Rasp. PI 2: ~16.5 seconds/image
Timing Results

78. SVM: Support Vector Machine,
Artificial intelligence, better
threshold analyzation, GPU readyFuture Work

79. Bad Red
Future work: Try the proposed
method but after luminance
calculation, toss red and
continue

81. Bad Red

82. Conclusions
High False positive rates (fixable with SVM)
Found every area that could contain areas of early cancer

83. Note*
All software libraries open source
All IDE’s, text editors and pdf creators open source
Open source inline with Grand Challenges community
Open source important to technological advancements

84. References
[ 1 ] S. K. Bram van Ginneken and C. Shneider., 2012-2016. Available: http:
//endovissub-barrett.grand-challenge.org/
[ 2 ] S. K. Bram van Ginneken and C. Shneider., 2012-2016. Available: http:
//grand-challenge.org/
[ 3 ] http://www.clipartbest.com/clipart-bTyoR8Xrc
[ 4 ] "Lenna 97: A Complete Story of Lenna". Dr. Lai Man P web pages. City
University of Hong Kong. 28 July 1997. Archived from the original on 2012-12-04.
Retrieved 24 September 2013.

85. References
[ 5 ] http://www.clker.com/clipart-187837.html
[ 6 ] D. Daniel. (2011, dec) Fractal dimension. [Online]. Available: http://soft-
matter.seas.harvard.edu/index.php/Fractal Dimension
[ 7 ] D. Nexus. (2012, dec) Gorgeous hair and amazing eyes. [Online]. Available:
https://people.desktopnexus.com/wallpaper/1334970/

86. References
[ 8 ] D. Bryant. (2013, mar) Fourier transforms. [Online]. Available: http:
//devonbryant.github.io/images/fourier/fourier transform.png
[ 9 ] J. Yamaguchi, A. Yoneyama, and T. Minamoto, “Automatic detection of early
esophageal cancer from endoscope image using fractal dimension and discrete
wavelet transform,” in Information Technology - New Generations (ITNG), 2015
12th International Conference on, April 2015, pp. 317–322.
[ 10 ] D. Pearcy. (2013, may) Introduction to fractal geometry. [Online]. Available:
https://danpearcymaths.files.wordpress.com/2013/05/image21.png