School of Information and Mechatronics
Signal and Image Processing Laboratory
Wook-Jin Choi
• Introduction
• Lung Volume Segmentation
• Genetic Programming based Classifier
• Hierarchical Block-image Analysis
• Shape-based Feature Descriptor
• Experimental Results
• Conclusions
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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
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(a) male (b) female
Trends in death rates for selected cancers, United States, 1930-2008
• 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
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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 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
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water
waterx1000
HU
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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
• 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
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• 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
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General structure of pulmonary nodule detection system
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
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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 pulmonary nodules
• The database consists of 84 CT scans
– 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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• 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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• Air has an attenuation of -1000 HU
• Most lung tissue is in the range of -910 HU to -500 HU
• The chest wall, blood vessel, and bone are above -500 HU
• The low and high intensities are differentiable around the intensity -500 HU
( , , ) ( , , ) 500i x y z I x y z HUS
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Input CT images, their intensity histograms, and thresholded images
• 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
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(0) 500T HU
( 1)
2
i o bT
( )iT
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Input CT images, their intensity histograms, and thresholded images
• 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
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18-connectivity voxels
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Labeled images after applying 3-D connected component labeling
• 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
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|lung first secondS l l
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Binary images of the selected lung region
Lung mask images after hole filling
• The contour of the lung volume is needed to
correct
– To include wall side nodule (juxta-pleural nodule)
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Extracted lung region using 3D connected component labeling and contour
corrected lung region (containing wall side nodule)
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Contour correction using chain-code representation
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• 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
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• The traditional multi-thresholding method
needs many steps of grey levels
• An iterative approach is applied to select
the threshold value
• The optimal threshold value is calculated
on median slice of lung CT scan
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( 1)
2
i o bT
• 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
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• 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
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Rule Description
R1 Small noise
R2 Vessel
R3 Large noise
R4 Nodule
Pruning rules for nodule candidate detection
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(d) (e) (f)
The results of nodule candidate detection: (a,d) ROIs, (b,e) vessel, and (c,f) nodule
candidates after rule-based pruning
(a) (b) (c)
• The features are useful information that describe characteristics of the nodule candidates
• In the proposed CAD system, these features will be used to train the GPC
• The proposed feature extraction process consists of two stages – The variety types of features are extracted from
the nodule candidates
– Subsets of features are selected and combined into sub-groups
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Index Feature Index Feature
2-D geometric features Mean inside
Area Mean outside
Diameter Variance inside
Perimeter Skewness inside
Circularity Kurtosis inside
3-D geometric features Eigenvalues
Volume 3-D intensity based statistical features
Compactness Minimum value inside
Bounding Box Dimensions Mean inside
Principal Axis Length Mean outside
Elongation Variance inside
2-D intensity based statistical features Skewness inside
Minimum value inside Kurtosis inside
1f
2f
3f
4f
5f
6f
97 ~ ff
1210 ~ ff
13f
14f
15f
16f
17f
18f
19f
2720 ~ ff
28f
29f
30f
31f
32f
33f
Features for nodule detection
Feature vector Description
2-D geometric features
3-D geometric features
2-D intensity-based statistical features
3-D intensity-based statistical features
2-D features
3-D features
Geometric features
Intensity-based statistical features
All features
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1 1 4{ ,..., }f ff
2 5 13{ ,..., }f ff
43 71 2{ ,..., }f ff
4 28 33{ ,..., }f ff
5 1 3f f f
6 2 4f f f
7 1 2f f f
8 3 4f f f
1 2 3 4f f f f f
Eight different groups of feature vectors
• Genetic Programming (GP) – An evolutionary
optimization technique
• The basic structure of GP is very similar to Genetic Algorithm(GA)
• The chromosome – GA : variable (binary digit
or string)
– GP : program (tree or graph)
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A function represented as a tree structure
• GP chromosome – The terminal set
• The elements of feature vector extracted from nodule candidate images
• Randomly generated constants with in the range 0,1
– The function set • Four standard arithmetic operator namely plus, minus,
multiply and division
• Additional mathematical operators log, exp, abs, sin and cos
• All operators in the function set are protected to avoid exception
• GP evolves combination of the terminal set and function set
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• Fitness Function – evaluate every individuals in GP generation
• True positive rate (TPR)
• Specificity (SPC) – SPC is the value subtracted from 1 to FPR and also called true negative
rate (TNR)
• Area under the ROC curve (AUC) – ROC curve is plotted between TP and FP for different threshold values
– AUC is area under the ROC curve and a good measure of classifier performance in different condition
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TPTPR
TP FN
1 1TN FP
SPC FPRTN FP FP TN
f TPR FPR AUC
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Objective To evolve a optimum classifier with a maximum TPR, SPC and AUC
Function Set +,-,*,protected division, log, exp, abs, sin and cos
Terminal Set Elements of a feature vector and randomly generated constants
Fitness Fit(B)=TPR×SPC×AUC
Selection Generational
Wrapper Positive if , else negative
Population Size 300
Generation Size 80
Initial Tree Depth Limit 6
Initial population Ramped half and half
GP Operators prob. Variable ratio of crossover mutation is used
Sampling Tournament
Survival mechanism Keep the best individuals
Real max. tree level 30
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Flow chart for training the proposed GPC
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Feature spaces for four types of features
2-D geometric feature 3-D geometric feature
2-D intensity-based statistical feature 3-D intensity-based statistical feature
• Examples of GPC expression
– log(log(log(times(log(f_{20}),times(abs(log(log(times( times(f_{5},l
og(f_{31})),log(abs(log(log(log(times(log(f_{9}),log(f_{31}))))))))))),lo
g(times(times(log(f_{5}),log(log( times(times(f_{5},log(log(f_{5}))),ti
mes(times(f_{5}, log(f_{9})),log(f_{9})))))),log(f_{31}))))))))
– plus(plus(plus(plus(plus(plus(f_{4},log(times(f_{11},plus(log(plus(f_
{9},plus(log(f_{11}),f_{4}))),f_{4})))),f_{4}),plus(log(plus(sin(log(abs(ti
mes(f_{11},plus(log(f_{4}),f_{4}))))),f_{4})),f_{4})),log(log(log(times(f_
{4},abs(f_{2})))))),log(plus(log(f_{10}),times(f_{1},abs(log(log(times(f
_{10},abs(f_{9}))))))))),log(log(times(log(log(times(f_{11},plus(log(ti
mes(log(log(times(f_{11},plus(log(f_{4}),
f_{4})))),f_{1})),f_{4})))),f_{1}))))
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Tree representation of the GPC expression
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Transformed features and classification threshold generated using a GPC
* Nodule
+ Non-nodule
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Training Performance on the training set Performance on testing set
Feature set Fitness Accuracy Sensitivity Specificity Accuracy Sensitivity SPC
0.979 99.1% 98.1% 100.0% 78.0% 70.0% 86.0%
0.954 97.8% 95.6% 100.0% 80.5% 86.5% 74.5%
0.859 94.4% 90.6% 98.1% 74.3% 73.0% 75.7%
0.741 90.9% 91.9% 90.0% 61.3% 64.2% 58.3%
0.972 98.8% 98.1% 99.4% 82.3% 82.3% 82.3%
0.951 98.1% 98.1% 98.1% 84.0% 90.7% 77.3%
0.986 99.4% 98.8% 100.0% 86.3% 87.3% 85.3%
0.858 94.7% 93.1% 96.3% 74.3% 74.2% 74.3%
0.988 99.4% 100.0% 98.8% 83.8% 89.2% 78.5%
0.026 1.3% 0.0% 2.6% 4.8% 6.0% 7.9%
Min 0.938 96.9% 100.0% 93.8% 76.7% 76.7% 66.7%
Max 1.000 100.0% 100.0% 100.0% 89.2% 98.3% 90.0%
1f
2f
3f
4f
5f
6f
7f
8ff
GPC results for different feature vectors using a 20–80 dataset
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Training Performance on the training set Performance on the testing set
Feature set Fitness Accuracy Sensitivity Specificity Accuracy Sensitivity SPC
0.876 94.9% 95.3% 94.5% 80.8% 73.7% 87.9%
0.865 94.5% 90.0% 98.9% 86.1% 85.8% 86.3%
0.764 90.9% 88.4% 93.4% 78.9% 75.5% 82.4%
0.628 85.5% 87.1% 83.9% 70.0% 74.5% 65.5%
0.925 96.8% 96.1% 97.6% 88.9% 89.7% 88.2%
0.907 96.2% 93.9% 98.4% 85.7% 85.5% 85.8%
0.940 97.5% 96.8% 98.2% 85.5% 88.7% 82.4%
0.751 90.1% 88.7% 91.6% 80.8% 81.3% 80.3%
0.919 96.7% 95.1% 98.4% 92.3% 94.0% 90.7%
0.028 1.0% 2.0% 1.1% 5.2% 8.0% 5.6%
Min 0.855 94.3% 90.2% 96.7% 83.3% 80.0% 80.0%
Max 0.943 97.5% 96.7% 100.0% 96.7% 100.0% 100.0%
1f
2f
3f
4f
5f
6f
7f
8ff
GPC results for different feature vectors using a 50–50 dataset.
Training Performance on the training set Performance on the testing set
Feature set Fitness Accuracy Sensitivity Specificity Accuracy Sensitivity SPC
0.874 95.0% 93.3% 96.7% 88.3% 88.0% 88.7%
0.890 95.4% 93.3% 97.5% 87.3% 86.0% 88.7%
0.709 89.1% 85.2% 93.0% 81.0% 81.3% 80.7%
0.557 82.0% 87.7% 76.4% 69.3% 78.7% 60.0%
0.872 94.9% 93.3% 96.6% 90.0% 92.0% 88.0%
0.855 94.2% 92.0% 96.4% 88.7% 87.3% 90.0%
0.923 96.8% 96.1% 97.5% 89.3% 89.3% 89.3%
0.723 89.4% 86.9% 92.0% 83.0% 78.7% 87.3%
0.889 95.5% 93.6% 97.4% 89.0% 96.0% 82.0%
0.049 1.8% 3.7% 2.1% 5.2% 4.7% 11.4%
Min 0.829 93.4% 88.5% 91.8% 80.0% 86.7% 60.0%
Max 0.945 97.5% 98.4% 98.4% 96.7% 100.0% 100.0%
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1f
2f
3f
4f
5f
6f
7f
8ff
GPC results for different feature vectors using a 80–20 dataset.
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• Coarse to fine hierarchical
block-image analysis
– Block size : 32, 24, 16, 12, 8
• 3-D CT scan is split into 3-
D block-images
• The non-informative
block-images are filtered
out by using entropy
analysis
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Result images after block splitting with respect to various block sizes
• Calculate the entropy H(x) on block image
• Select informative blocks by using entropy
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1
2 2
1
1( ) ( ) log ( ) log ( )
( )
n n
i i
H x p i p i p ip i
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The entropy histograms of block-images for five different block sizes
(x-axis : entropy value, y-axis : number of blocks, (a) 32, (b) 24, (c) 16, (d) 12, and (e) 8)
• The selected block-
image is enhanced
• The object in the
selected block-image
is segmented
• The location of block
image is adjusted
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• Block-image enhancement is presented for more accurate analysis
• 3-D coherence-enhancing diffusion (CED) filter – Hessian matrix based
– Preserve small spherical structure (nodule)
– Enhance tubular structure (vessel)
(a) Input image and (b) the result image after enhancement
• Optimal threshold
– Iterative approach
– Initial threshold : -500HU
– Threshold converges, and optimal threshold
obtained
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• The location of block-image should be adjusted – The segmented object is not located in the center
of the block
• Block location is iteratively updated by using centroid of the segmented object
• The iteration of the adjustment continues until the center position converges
• Or distance between the adjusted location and the original location is larger than half of the block size
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Iterations of automatic block location adjustment, upper: 3-D shapes, lower: the
median slices of 3-D block; (a) the first; (b) the fifth; and (c) the last iterations of
adjustment
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• Three different types of features are
extracted from nodule candidate block-
images
• Nodule has their own shapes
– Important characteristics to distinguish
• 2-D and 3-D geometric features describe
the shape of nodule candidates
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Features for nodule detection
• Support vector machine (SVM)
– SVM is a useful technique for data
classification
– Supervised learning models with associated
learning algorithms
– SVM analyze data and recognize patterns
– Classification and regression analysis
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• The basic SVM takes a set of input data and predicts two possible classes for each given input
• Training dataset
• The SVM requires the solution of the following optimization problem
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• SVM can efficiently perform non-linear classification using the kernel trick
• Kernel function
– Polynomial function
– Radial basis function
– Minkowski distance function
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• k-fold cross-validation is applied to
evaluated the proposed classifier
• Performance validation measure
– The number of true positives (TPs) and false
positives (FPs)
– Accuracy, sensitivity, specificity, and area
under the ROC curve.
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k p AUC Accuracy Sensitivity Specificity
5 0.25 0.9738 91.52% 87.16% 95.88%
7 0.25 0.9784 93.97% 91.02% 96.92%
10 0.25 0.9736 92.43% 88.97% 95.88%
The k-fold cross validation results of SVM classifiers with radial basis
function kernel for different k values
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p AUC Accuracy Sensitivity Specificity
SVM-r 0.1 0.9727 84.72% 69.44% 100.00%
0.125 0.9746 88.96% 78.70% 99.23%
0.25 0.9784 93.97% 91.02% 96.92%
0.5 0.9754 92.82% 91.54% 94.10%
1 0.9712 91.79% 91.53% 92.05%
2 0.9673 92.30% 93.08% 91.53%
SVM-p 0.1 0.4660 47.40% 0.00% 94.81%
0.125 0.4632 44.81% 0.26% 89.35%
0.25 0.6876 68.26% 86.13% 50.39%
0.5 0.9462 89.85% 91.52% 88.18%
1 0.9463 90.74% 92.78% 88.69%
2 0.9646 92.29% 91.25% 93.32%
SVM-m 0.1 0.8706 82.55% 86.91% 78.19%
0.125 0.7051 69.71% 78.46% 60.95%
0.25 0.5706 60.68% 68.69% 52.68%
0.5 0.5469 59.02% 66.63% 51.41%
1 0.5420 58.11% 66.11% 50.12%
2 0.5527 57.60% 65.85% 49.36%
The 7-fold cross validation results of SVM classifiers with three different kernel functions, SVM-r: radial basis function,
SVM-p: polynomial function, and SVM-m: Minkowski distance function
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ROC curves of the SVM classifiers with respect to three different kernel functions,
SVM-r: radial basis function, SVM-p: polynomial function, and SVM-m: Minkowski
distance function; (a) p = 0:25 and (b) p = 1.
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• Eigenvalue decomposition
of Hessian Matrix
– Dot enhancement filter
– Feature extraction
• Multi-scale dot
enhancement filter
– Enhance the nodules
– The shape of nodules is like
dot or ball
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• Multi-scale dot enhancement filter is
based on eigenvalue of Hessian matrix
• Hessian Matrix
• Local structure information is obtained by
Hessian matrix
• Eigenvalue decomposition of Hessian Matrix
– Structure information : surface-ness, curve-ness, and point-
ness
– This information is expressed in the three singular tensors (stick, plate, and ball)
• Tensor based representation
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• Stick tensor
• Plate tensor
• Ball tensor
• Surface-ness : saliency , orientation
• Curve-ness : saliency , orientation
• Point-ness : saliency , no orientation
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• The dot enhancement filter is applied to enhance the spherical object, such as nodule
• For each voxel, the dot value is defined as
• are three eigenvalues from the Hessian matrix
• Gaussian image smoothing with a variety scales is performed prior to the calculation of the gradient for different size of nodules and reducing noise
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• Assuming that the diameter of nodule to be detected are in a range the N discrete smoothing scales ___ in the range of can be calculated as
where and each scale has corresponding nodule diameter
• The maximum dot value calculated among the different smoothing scales
• Five steps smoothing scales are used in the range of nodule diameter [3mm, 30mm]
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(1/( 1))
1 0( / ) Nr d d
• The image block is extracted as a potential nodule candidate
– The dot values are larger than predefined threshold
• The dimension of the image block is
• It is noted that the size of the image block is considered at the relation to the corresponding smoothing scale as follows:
where the braces indicate the ceiling function
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• A novel shape-based feature extraction method is proposed
• Angular Histogram of Surface Normal Feature
• The feature extraction has important role in the pulmonary nodule CAD system
• The detected nodule candidates are considered as nodules or non-nodules using the extracted feature information
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• Popular approach in the last decade for 2-D images
• The scale invariant feature transform (SIFT) – It can extract salient points and feature descriptors in the
most invariant way with respect to scaling, translation, orientation, affine changes and illumination within images
– The SIFT is designed and tested on 2-D images of 3-D object.
– Allaire et al. proposed fully orientation invariant 3-D SIFT
• The histograms of oriented gradients (HOG) – Describing salient points on 2-D images of 3-D objects
– Scherer et al. proposed the 3-D extension of HOG is proposed for 3-D object retrieval
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• The shape-based feature descriptor is extracted for small 3-D object in image patch
• The AHSN feature extraction method is proposed to analyze the shape of the target object
• The eigenvalue decomposition of the Hessian matrix is applied to every voxels for target image
• The histograms are obtained on surface-ness information
– surface saliency :
– surface normal vector :
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• The orientation of surface normal vector is obtained prior to calculate AHSN feature based on the eigenvalue decomposition of the Hessian matrix
• The orientation of surface normal vector is represented as two kinds of orientation in spherical coordination
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• Two angular histograms are constructed – The orientation θ histogram with n bins is formed
• Each bin covering 180/n degrees
• Each sample in the image block added to a histogram bin is weighted by its surface-ness saliency and normalized by total sum of surface-ness saliency
– The orientation φ is quantized into n bins • Each bin covering 360/n degrees
• Each sample in the image block added to a histogram bin is weighted and normalized
– The dimension of feature descriptor is 2n
– The extracted AHSN feature is scale-invariant
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The extracted AHSN feature for a sphere (nodule model), left –
reconstructed 3-D shape, center - orientation θ histogram, right -
orientation φ histogram
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The extracted AHSN feature for a cylinder (vessel model), left –
reconstructed 3-D shape, center - orientation θ histogram, right -
orientation φ histogram
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The extracted AHSN feature for a curved surface (wall model), left –
reconstructed 3-D shape, center - orientation θ histogram, right -
orientation φ histogram
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The extracted AHSN feature for a pulmonary nodule, left – reconstructed
3-D shape, center - orientation θ histogram, right - orientation φ
histogram
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The extracted AHSN feature for a pulmonary vessel, left – reconstructed
3-D shape, center - orientation θ histogram, right - orientation φ
histogram
• Lung wall influence the detection accuracy
• For more accurate nodule detection, walls
are eliminated from image blocks of
nodule candidates
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Comparison of AHSN feature for a juxta-pleural nodule at before (1st
row) and after (2nd row) wall elimination, left - reconstructed 3-D shape,
center - orientation θ histogram, right - orientation φ histogram
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Comparison of AHSN feature for a solid nodule at before (1st row) and
after (2nd row) wall elimination, left - reconstructed 3-D shape, center -
orientation θ histogram, right - orientation φ histogram
• The extracted AHSN feature vectors are
analyzed by SVM classifier
• SVM is a useful technique for data
classification
• k-fold cross-validation is applied to
evaluated the proposed classifier (k = 10)
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Classfier Accuracy Sensitivity Specificity
Before LWE SVM-p 96.4% 98.4% 94.3%
SVM-r 97.8% 98.7% 96.9%
SVM-m 93.9% 95.9% 92.0%
After LWE SVM-p 97.0% 97.9% 96.1%
SVM-r 97.8% 97.4% 98.2%
SVM-m 94.5% 94.6% 94.3%
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The results of 10-fold cross validation on different kernel functions using
SVM as a classier before and after wall elimination (LWE)
Descriptor Accuracy Sensitivity Specificity
SVM-p Gradient 95.1% 96.4% 93.8%
Hessian Matrix 97.0% 97.9% 96.1%
SVM-r Gradient 96.1% 96.4% 95.9%
Hessian Matrix 97.8% 97.4% 98.2%
SVM-m Gradient 92.8% 93.0% 92.6%
Hessian Matrix 94.5% 94.6% 94.3%
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The results of 10-fold cross validation on with four different kernel
functions based SVMs for the descriptors using gradient and Hessian
matrix
Descriptor Accuracy Sensitivity Specificity
SVM-p AHSN 180 97.0% 97.9% 96.1%
AHSN 90 96.9% 97.4% 96.4%
AHSN 72 96.9% 98.5% 95.3%
AHSN 36 96.0% 97.4% 94.6%
3-D SIFT 128 92.9% 93.3% 92.5%
3-D HOG 468 95.2% 96.7% 93.8%
3-D HOG 216 94.2% 95.1% 93.3%
SVM-r AHSN 180 97.8% 97.4% 98.2%
AHSN 90 97.5% 97.2% 97.9%
AHSN 72 97.6% 97.4% 97.7%
AHSN 36 96.5% 96.9% 96.1%
3-D SIFT 128 36.2% 8.4% 100.0%
3-D HOG 468 77.2% 36.5% 100.0%
3-D HOG 216 89.3% 78.9% 99.7%
SVM-m AHSN 180 94.5% 94.6% 94.3%
AHSN 90 95.2% 95.3% 95.1%
AHSN 72 95.8% 96.2% 95.4%
AHSN 36 94.9% 95.4% 94.3%
3-D SIFT 128 88.9% 87.9% 89.9%
3-D HOG 468 94.0% 90.9% 94.0%
3-D HOG 216 94.7% 94.3% 95.1%
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The results of 10-fold cross validation for the different descriptors on various kernel functions
of SVM classifier
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ROC curves of the SVM classifiers with respect to three different kernel
functions, SVM-r: radial basis function, SVM-p: polynomial function, and SVM-m:
Minkowski distance function; (a) p = 0:25 and (b) p = 1.
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Nodules Non-nodules
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(a) (b)
The result of pulmonary nodule detection: (a) 43rd slice, (b) 3-D
representation, the detected nodules are indicated by a red color and the
non-nodules are indicated by a white color
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AUC Accuracy Specificity Sensitivity FPs/scan
Nodule Candidates Detection 96.6% 51.25
20-80 0.921 76.6% 75.9% 88.3% 12.32
50-50 0.960 86.7% 86.4% 91.7% 6.99
80-20 0.967 89.6% 89.3% 90.9% 5.45
The results of CAD system using GP based classifier
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FROC curves of the GPC with respect to three training and testing
datasets
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AUC Accuracy Specificity Sensitivity FPs/scan
Nodule Candidates Detection 97.3% 60.21
0.1 0.9931 95.89% 99.62% 92.67% 0.23
0.125 0.9934 96.92% 99.11% 93.95% 0.54
0.25 0.9929 97.61% 96.23% 95.28% 2.27
0.5 0.9835 95.15% 93.93% 92.85% 3.65
1 0.9727 92.98% 92.33% 90.63% 4.62
2 0.9584 92.41% 89.74% 90.45% 6.18
The results of CAD system using Hierarchical Block-image Analysis
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FROC curves of the proposed CAD system with respect to three different
kernel parameters of SVM-r classifiers
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The overall performance of CAD system for different parameters p of SVM-r
classifiers
AUC Accuracy Specificity Sensitivity FPs/scan
Nodule Candidates Detection 97.9% 135.39
AHSN 180 0.9945 97.8% 98.2% 95.4% 2.43
AHSN 90 0.9923 97.5% 97.9% 95.2% 2.84
AHSN 72 0.9895 97.6% 97.7% 95.4% 3.11
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FROC curves of the proposed CAD system with respect to three different
dimensions of AHSN features
CAD systems Nodule size FPs per case Sensitivity
Suzuki et al.(2003)[26] 8 - 20 mm 16.1 80.3%
Rubin et al.(2005)[27] >3 mm 3 76%
Dehmeshki et al.(2007)[28] 3 - 20 mm 14.6 90%
Suarez-Cuenca et al.(2009)[29] 4 - 27 mm 7.7 80%
Golosio et al.(2009)[30] 3 - 30 mm 4.0 79%
Ye et al.(2009)[31] 3 - 20 mm 8.2 90.2%
Sousa et al.(2010)[32] 3 - 40.93 mm - 84.84%
Messay et al.(2010)[33] 3-30 mm 3 82.66%
Riccardi et al.(2011)[34] >3 mm 6.5 71.%
Cascio et al.(2012)[35] 3-30 mm 6.1 97.66%
Genetic Programming 3-30 mm 5.45 90.9%
Hierarchical Block Analysis 3-30 mm 2.27 95.2%
Shape-based Feature 3-30 mm 2.43 95.4%
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• Automated pulmonary nodule detection system is studied
• Pulmonary nodule detection CAD system is an effective solution for early detection of lung cancer
• The proposed systems are based on
– Genetic programming based classifier
– Hierarchical block-image analysis
– 3-D shape-based feature descriptor
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• The performance of the proposed CAD systems is evaluated on the LIDC database of NCI
• The GPC based system was shown to significantly reduce the false positives while maintaining a high sensitivity – 5.45 FPs/scan, 90.9% sensitivity
• The hierarchical block-image analysis based system has shown more accurate result with improved local object segmentation – 2.27 FPs/scan, 95.28% sensitivity
• Shape-based feature descriptor was applied the nodule detection CAD system that has shown higher accuracy and robustness than conventional descriptor – 2.43 FPs/scan, 95.4% sensitivity
• The proposed methods have significantly reduced the false positives in nodule candidates
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