1. School of Information and Mechatronics
Signal and Image Processing Laboratory
Wook-Jin Choi

2. • Introduction
• Lung Segmentation
• Nodule Candidates Detection
• Optimal Fuzzy Rule-based Pruning
• Experimental Results
• Conclusions
2

3. • Lung cancer is the leading cause of cancer
deaths.
• Most patients diagnosed with lung cancer
already have advanced disease
– 40% are stage IV and 30% are III
– The current five-year survival rate is only 16%
• Defective nodules are detected at an early
stage
– The survival rate can be increased
3

4. • Early detection of lung nodules is
extremely important for the diagnosis and
clinical management of lung cancer
• Lung cancer had been commonly detected
and diagnosed on chest radiography
• Since the early 1990s CT has been
reported to improve detection and
characterization of pulmonary nodules
4

5. • CT was introduced in 1971
– Sir Godfrey Hounsfield, United Kingdom
• CT utilize computer-processed X-rays
– to produce tomographic images or 'slices' of specific
areas of the body
• The Hounsfield unit (HU) scale is a linear
transformation of the original linear attenuation
coefficient measurement into one in which the
radio density of distilled water
5
 
x water 1000

water
HU  

6. 6
Substance HU
Air −1000
Lung −500
Fat −84
Water 0
Cerebrospinal Fluid 15
Blood +30 to +45
Muscle +40
Soft Tissue, Contrast Agent +100 to +300
Bone +700(cancellous bone)to +3000 (dense bone)
The HU of common substances
Nodule

7. • Lung cancer screening is currently implemented
using low-dose CT examinations
• Advanced in CT technology
– Rapid image acquisition with thinner image sections
– Reduced motion artifacts and improved spatial
resolution
• The typical examination generates large-volume
data sets
• These large data sets must be evaluated by a
radiologist
– A fatiguing process
7

8. • The use of pulmonary nodule detection CAD
system can provide an effective solution
• CAD system can assist radiologists by increasing
efficiency and potentially improving nodule
detection
8
General structure of pulmonary nodule detection system

9. CAD systems Lung segmentation Nodule Candidate Detection False Positive Reduction
Suzuki et al.(2003)[26] Thresholding Multiple thresholding MTANN
Rubin et al.(2005)[27] Thresholding Surface normal overlap
Lantern transform and rule-ba
sed classifier
Dehmeshki et al.(2007)[28] Adaptive thresholding Shape-based GATM Rule-based filtering
Suarez-Cuenca et al.(2009)[29]
Thresholding and 3-D connec
ted component labeling
3-D iris filtering
Multiple rule-based LDA classi
fier
Golosio et al.(2009)[30] Isosurface-triangulation Multiple thresholding Neural network
Ye et al.(2009)[31]
3-D adaptive fuzzy segmenta
tion
Shape based detection
Rule-based filtering and weig
hted SVM classifier
Sousa et al.(2010)[32] Region growing Structure extraction SVM classifier
Messay et al.(2010)[33]
Thresholding and 3-D connec
ted component labeling
Multiple thresholding and mo
rphological opening
Fisher linear discriminant and
quadratic classifier
Riccardi et al.(2011)[34] Iterative thresholding
3-D fast radial filtering and sc
ale space analysis
Zernike MIP classification bas
ed on SVM
Cascio et al.(2012)[35] Region growing Mass-spring model
Double-threshold cut and neu
ral network
9

10. • Thresholding
– Fixed threshold
– Optimal threshold
– 3-D adaptive fuzzy thresholding
• Lung region extraction
– 3-D connectivity with seed point
– 3-D connected component
labeling
• Contour correction
– Morphological dilation
– Rolling ball algorithm
– Chain code representation
10

11. • A fixed threshold is applicable to segment lung
area
– The intensity ranges of images are varied by different
acquisition protocols
• To obtain optimal threshold
– Iterative approach continues until the threshold
converges
– The initial threshold :
– is i th threshold and new threshold as
11
T(0)  500HU
   
T
( i 1)
o b 2

(i) T

12. 12
Input CT images, their intensity histograms, and thresholded images

13. • White areas
– non-body voxels
– including lung cavity
• Black areas
– body voxels
– excluding lung region
• Lung regions are
extracted from the non-body
voxels by using 3-
D connected
component labeling
13
18-connectivity voxels

14. 14
Labeled images after applying 3-D connected component labeling

15. • To extract lung volume
– Remove rim attached to boundaries of image
– The first and the second largest volumes are
selected as the lung region
• The lung region contains small holes
– To remove these holes
– Morphological hole filling operations are applied
15
Slung  l first | lsecond

16. 16
Binary images of the selected lung region
Lung mask images after hole filling

17. • The contour of the lung volume is needed to
correct
– To include wall side nodule (juxta-pleural nodule)
17
Extracted lung region using 3D connected component labeling and contour
corrected lung region (containing wall side nodule)

18. 18
Contour correction using chain-code representation

19. 19

20. 20

21. • Detection of nodule
candidates is important
• The performance of nodule
detection system relies on
the accuracy of candidate
detection
• ROI extraction
– Optimal multi-thresholding
• Nodule candidates
detection and segmentation
– Rule-based pruning
21

22. • The traditional multi-thresholding method
needs many steps of grey levels
• An iterative approach is applied to select
the threshold value
   
i o b T
• The optimal threshold value is calculated
on median slice of lung CT scan
22
( 1)
2


23. • The optimal threshold value
– A base threshold for multi-thresholding
• Additional six threshold values are obtained
– Base threshold + 400,+ 300,+ 200,+ 100, - 100,
and - 200
23

24. 24
Optimal Fuzzy Rule
based on GA
ROIs Nodule
Candidates

25. • Fuzzy rule based classifier removes vessels and noise
• Vessel removing
– Volume is extremely bigger than nodule
– Elongated object
• Noise removing
– Radius of ROI is smaller than 3mm
– Bigger than 30mm
• Remaining ROIs are nodule candidates
25
Index Feature
1 Area
2 Diameter
3 Circularity
4 Volume
5-8 Bounding Box Dimensions
9 Elongation

26. 26
Rule Description
R1 Small noise
R2 Vessel
R3 Large noise
R4 Nodule
Not precise
Not optimal
Pruning rules for nodule candidate detection

27. Input Fuzzy layer Rule layer Output
Σ Y
27
R1
R2
R3
GA based
Fuzzy Rule
Inducer
X1
X2
X3
X4
X5
F1
F2
F3
F4
F5
Optimal fuzzy rules are induced by using GA-Fuzzy Inference System

28. • A fuzzy inference system (FIS) is a system
that uses fuzzy set theory to map inputs
(features in the case of fuzzy classification)
to outputs (classes in the case of fuzzy
classification).
• Two FIS’s will be discussed here, the
Mamdani and the Sugeno.
28

29. 29

30. 30

31. (a) A fuzzy inference system and (b) a fuzzy inference system as neural network.
31

32. • Input
– Features extracted fromROIs
• Fuzzy layer
– Input features are fuzzified
– Fuzzy membership function is optimized by GA
• Rule layer
– Fuzzified features are combined as a optimal fuzzy
rule
– Weight matrix for linear combination is optimized by
GA
• Output
– Defuzzifipication of optimal fuzzy rules
32

33. • Chromosome
– Fuzzy membership function selection
• Sigmoidal membership function
• Negative sigmoidal membership function
• Product of two sigmoidal membership functions
• Gaussian membership function
– Parameters of the selected fuzzy membership function
• Fitness function
– Subtraction between average membership degree of
true and false data
33
d  t  f

34. • Chromosome
– Weight matrix for linear combinations of
fuzzified features
• Fitness function
– Balanced accuracy of classification results
34
TPR FPR
(1 )
2
BACC
 


35. • To evaluate the performance of the proposed method, Lung Image
Database Consortium (LIDC) database is applied
• LIDC database, National Cancer Institute (NCI), United States
– The LIDC is developing a publicly available database of thoracic
computed tomography (CT) scans as a medical imaging research
resource to promote the development of computer-aided
detection or characterization of pulmonary nodules
• The database consists of 84 CT scans (up to 2009)
– 100-400 Digital Imaging and Communication (DICOM) images
– An XML data file containing the physician annotations of nodules
– 148 nodules
– The pixel size in the database ranged from 0.5 to 0.76 mm
– The reconstruction interval ranged from 1 to 3mm
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36. 36
False positives in ROIs: 72466
Sensitivity
False positive
rate
Accuracy
Balanced
Accuracy
0.9800 0.6068 0.3965 0.6866
Performance of conventional rule-based pruning
False positives: 43970
False positives per scan : 523.4524

37. 37
Elongation Circularity

38. 38
AUROC = 0.9711

39. 39
Fitness Sensitivity
False positive
rate
Accuracy
Balanced
Accuracy
AUROC
1 0.8883 0.9825 0.2302 0.7709 0.8761 0.9711
2 0.8892 0.9775 0.2148 0.7862 0.8813 0.9708
3 0.8863 0.9900 0.2732 0.7282 0.8584 0.9699
4 0.8874 0.9875 0.2515 0.7498 0.8680 0.9692
5 0.8865 0.9900 0.2676 0.7338 0.8612 0.9737
6 0.8871 0.9875 0.2562 0.7452 0.8657 0.9711
7 0.8871 0.9900 0.2565 0.7449 0.8668 0.9745
8 0.8882 0.9800 0.2332 0.7679 0.8734 0.9692
9 0.8882 0.9875 0.2341 0.7672 0.8767 0.9708
10 0.8885 0.9875 0.2291 0.7720 0.8792 0.9709
mean 0.8877 0.9860 0.2446 0.7566 0.8707 0.9711
std 0.0009 0.0044 0.0190 0.0189 0.0078 0.0017
Performance of optimal fuzzy rule-based pruning
False positives: 17728
False positives per scan: 211.04

40. • Automated pulmonary nodule detection
system is studied
• Pulmonary nodule detection CAD system
is an effective solution for early detection
of lung cancer
• The proposed method are based on
optimal fuzzy rule
• The optimal fuzzy rule pruned unwanted
ROIs with higher sensitivity
40

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