A presentation from the annual Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2016 by Joshua Leibstein

1. Broadband Evolution - Unlocking
“The Internet of Things”
TB detection using
modified Local Binary Pattern features
Joshua Leibstein
Andre Nel

2. Overview
• Introduction
• The problem
• Radiograph normalisation
• Lung segmentation
• Classification
• Preliminary results
• Conclusion
• Future work

3. Introduction
• SA TB prevalence third highest globally
• 1% of SA population infected annually
• People infected with HIV at greater risk
• SANAC goal to reduce TB by 50% by 2016
• Image processing (IP) detection scheme

4. The Problem
Figure 1: Two radiographs captured from the same patient

5. • Influencing factors
– User settings
– Capturing equipment
– Post-processing
– Analogue digitisation
– Poorly trained/overworked radiographers
• Large variances between mine worker CXRs
• Normalisation required
The Problem

6. Normalisation
• Difference of Gaussian (DoG) energy
normalisation
Figure 2: The frequency band separation process

7. Normalisation
Figure 3: Frequency sub-bands 𝐼1 ⋯ 𝐼 𝑛

8. • Find weighting 𝜆𝑖 for each sub-band
• 𝜆𝑖 =
Dataset energy
Sample energy
(1)
• 𝐼 = 𝑖=1
𝑛
𝜆𝑖 𝐼𝑖 (2)
Normalisation
+ + + + + =
𝐼1 𝐼2 𝐼3 𝐼4 𝐼5 𝐼6

9. Normalisation
Figure 4: Three radiographs from the PHRU set

10. Normalisation
Figure 5: Energy normalised PHRU radiographs and reference
radiographs from the JSRT set

11. Segmentation
• Active Shape Models (ASMs)
– Supervised
– Manual segmentations defined by “landmarks”
– Sampled “whiskers”
– Principal Component Analysis (PCA)

12. Segmentation
Figure 6: Lungs delineated by a feature vector and
perpendicular whiskers

13. Segmentation
Figure 7: An extracted greyscale profile and resulting
normalised gradient

14. Segmentation
Figure 8: Fitting the mean shape to a sample radiograph

15. Segmentation
Figure 9: PHRU inputs and ASM segmentations

16. Segmentation
Figure 10: JSRT inputs, ASM segmentations and SCR ground truths

17. Overlap Measure
• Ω =
TP
TP + FP + FN
(3)
– TP: True Positive
– FP: False Positive
– FN: False Negative
• Ω = 87,5983,986%
Figure 11: Overlayed lung masks

18. Subdivision
Figure 12: Mean lung shape subdivision and RGB masks

19. RBF Interpolation
• Interpolation of 𝑛-dimensional scattered data
• 𝑠 𝐱 = 𝑖=1
𝑛
𝑤𝑖 𝜙 ∥ 𝐱 − 𝐱 𝑖 ∥ (4)
Figure 13: Interpolation of the mean shape onto a sample lung
segmentation

20. RBF Interpolation
Figure 14: The original position and the interpolated position of a
nodule onto the mean lung shape

21. RBF Interpolation
Figure 15: Abnormalities warped onto the mean shape

22. Probability Measure
Figure 16: Probability heat maps for regions 1 - 42
Max PMin P

23. Classification
• Local Binary Patterns (LBPs)
– Texture classification
– Uniform patterns
Figure 17: Feature vector of a 𝐿𝐵𝑃8,1
𝑟𝑖𝑢2
sample

24. Classification
• Circularly sampled
• Log-likelihood measure
• k-Nearest Neighbour
Figure 18: Circularly sampled 𝐿𝐵𝑃8,1
𝑟𝑖𝑢2
feature vectors

25. Results

26. Results

27. Results

28. Conclusion
• Energy normalisation using DoG
• ASM lung segmentation
• Probability measure
• Abnormality classification using LBPs
• Integrated detection system

29. Future work
• Relax segmentation parameters
• Interpolation of region boundaries
• Smaller training regions
• Addition of a shape descriptor
• Normalised vs unnormalised
• Additional probability measure

30. References
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