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Optimal Fuzzy Rule based Pulmonary Nodule Detection
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
Contents
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• 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
Introduction
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• Early detection of lung nodules is ex-tremely important for the diagnosis and clinical management of lung cancer
• Lung cancer had been commonly de-tected and diagnosed on chest radiogra-phy
• Since the early 1990s CT has been re-ported to improve detection and charac-terization of pulmonary nodules
Introduction
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• CT was introduced in 1971– Sir Godfrey Hounsfield, United Kingdom
• CT utilize computer-processed X-rays– to produce tomographic images or 'slices' of spe-
cific areas of the body
• The Hounsfield unit (HU) scale is a linear transformation of the original linear attenua-tion coefficient measurement into one in which the radio density of distilled water
Computed Tomography
water
waterx1000
HU
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Computed Tomography
The HU of common substances
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)
Nodule
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• 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 reso-
lution
• The typical examination generates large-vol-ume data sets
• These large data sets must be evaluated by a radiologist– A fatiguing process
Computed Tomography
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• The use of pulmonary nodule detection CAD sys-tem can provide an effective solution
• CAD system can assist radiologists by increasing efficiency and potentially improving nodule de-tection
Pulmonary Nodule Detection CAD system
General structure of pulmonary nodule detection system
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CAD systems Lung segmentationNodule Candidate De-
tectionFalse Positive Reduction
Suzuki et al.(2003)[26] Thresholding Multiple thresholding MTANN
Rubin et al.(2005)[27] Thresholding Surface normal overlapLantern transform and rule-based classifier
Dehmeshki et al.(2007)[28]
Adaptive thresholding Shape-based GATM Rule-based filtering
Suarez-Cuenca et al.(2009)[29]
Thresholding and 3-D connected component la-beling
3-D iris filteringMultiple rule-based LDA classifier
Golosio et al.(2009)[30] Isosurface-triangulation Multiple thresholding Neural network
Ye et al.(2009)[31]3-D adaptive fuzzy seg-mentation
Shape based detectionRule-based filtering and weighted SVM classifier
Sousa et al.(2010)[32] Region growing Structure extraction SVM classifier
Messay et al.(2010)[33]Thresholding and 3-D connected component la-beling
Multiple thresholding and morphological opening
Fisher linear discriminant and quadratic classifier
Riccardi et al.(2011)[34] Iterative thresholding3-D fast radial filtering and scale space analysis
Zernike MIP classification based on SVM
Cascio et al.(2012)[35] Region growing Mass-spring modelDouble-threshold cut and neural network
Pulmonary Nodule Detection CAD system
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Lung Volume Segmentation
• 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
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• 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 con-
verges– The initial threshold : – is i th threshold and new threshold as
Optimal Threshold
(0) 500T HU
( 1)
2i o bT
( )iT
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Optimal Threshold
Input CT images, their intensity histograms, and thresh-olded images
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Lung Region Extraction
• White areas– non-body voxels – including lung cavity
• Black areas– body voxels– excluding lung region
• Lung regions are ex-tracted from the non-body voxels by using 3-D connected com-ponent labeling18-connectivity voxels
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Lung Region Extraction
Labeled images after applying 3-D connected component la-beling
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• 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 ap-
plied
Lung Region Extraction
|lung first secondS l l
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Lung Region Extraction
Binary images of the selected lung region
Lung mask images after hole filling
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• The contour of the lung volume is needed to correct– To include wall side nodule (juxta-pleural nodule)
Contour Correction
Extracted lung region using 3D connected component labeling and contour corrected lung region (containing wall side nodule)
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Contour Correction
Contour correction using chain-code representation
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Segmented Lung Volume
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Results
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Nodule Candidate Detec-tion
• Detection of nodule candi-dates is important
• The performance of nodule detection system relies on the accuracy of candidate detection
• ROI extraction– Optimal multi-thresholding
• Nodule candidates detec-tion and segmentation– Rule-based pruning
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• The traditional multi-thresholding method needs many steps of grey levels
• An iterative approach is applied to se-lect the threshold value
• The optimal threshold value is calcu-lated on median slice of lung CT scan
Optimal Multi-thresholding
( 1)
2i o bT
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• The optimal threshold value– A base threshold for multi-thresholding
• Additional six threshold values are ob-tained– Base threshold + 400,+ 300,+ 200,+ 100, -
100, and - 200
Optimal Multi-thresholding
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Nodule Candidtes Detection
Optimal Fuzzy Rulebased on GA
ROIs Nodule Candidates
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• 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
Rule-based Pruning
Index Feature
1 Area
2 Diameter
3 Circularity
4 Volume
5-8 Bounding Box Dimensions
9 Elongation
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Conventional Rule-based Pruning
Rule
Description
R1 Small noise
R2 Vessel
R3 Large noise
R4 Nodule
Pruning rules for nodule candidate detection
Not preciseNot optimal
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Optimal Fuzzy Rule-based Pruning
Σ Y
R1
R2
R3
GA basedFuzzy Rule
Inducer
X1
X2
X3
X4
X5
F1
F2
F3
F4
F5
Input Fuzzy layer Rule layer Output
Optimal fuzzy rules are induced by using GA-Fuzzy Inference System
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• 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 classifi-cation).
• Two FIS’s will be discussed here, the Mamdani and the Sugeno.
Fuzzy Inference Systems
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Fuzzy Inference Systems (Mam-dani)
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Fuzzy Inference System (Sugeno)
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Fuzzy Inference Systems
(a) A fuzzy inference system and (b) a fuzzy inference system as neu-ral network.
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• 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
GA-Fuzzy Inference System
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• 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
Fuzzy Membership Function Op-timization
t fd
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• Chromosome–Weight matrix for linear combinations of
fuzzified features
• Fitness function– Balanced accuracy of classification re-
sults
GA basedFuzzy Rule Inducer
(1 )
2
TPR FPRBACC
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• 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 pul-monary 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
Experimental Data Set
36
Experimental Results
Sensitivity False posi-tive rate Accuracy Balanced
Accuracy
0.9800 0.6068 0.3965 0.6866
Performance of conventional rule-based pruning
False positives: 43970False positives per scan : 523.4524
False positives in ROIs: 72466
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Experimental Results
Elongation Circularity
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Experimental Results
AUROC = 0.9711
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Experimental Results
Fitness SensitivityFalse posi-tive rate
AccuracyBalancedAccuracy
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: 17728False positives per scan: 211.04
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• Automated pulmonary nodule detec-tion system is studied
• Pulmonary nodule detection CAD sys-tem is an effective solution for early detection of lung cancer
• The proposed method are based on optimal fuzzy rule
• The optimal fuzzy rule pruned un-wanted ROIs with higher sensitivity
Conclusions
41
Q & A
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THANK YOU
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