Computer Aided Detection Algorithm for Lung Disease using Multi-Detector CT

1. Computer Aided Detection
Algorithm for Lung Disease
using Multi-Detector CT
심사위원장 : 조 규 성
심 사 위 원 : 조 남 진
조 성 오
예 종 철
김 진 환
김 진 성
Dept. of Nuclear and Quantum Eng. KAIST

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Contents
1. Introduction
1. Computer Aided Diagnosis (CAD)
2. CAD for Lung Disease
3. Practical Problems
2. Objective and Scope of Work
1. Objective and Scope of work
2. Summary of Proposal
3. Solid Pulmonary Nodule Detection Algorithm
4. Ground Glass Opacity Detection Algorithm
5. Conclusion
Introduction Objective & Scope SPN Detection ConclusionGGO Detection

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Contents
1. Introduction
1. Computer Aided Diagnosis (CAD)
1. What is CAD?
2. Effectiveness of CAD
3. Application of Various CAD
2. CAD for Lung Disease
1. Lung Cancer Overview
2. What is SPN & GGO?
3. Research Trend
3. Practical Problems
2. Objective and Scope of Work
3. Solid Pulmonary Nodule Detection Algorithm
4. Ground Glass Opacity Detection Algorithm
5. Further Study
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Computer Aided Diagnosis (CAD)
What is CAD?

Computer-Aided Diagnosis

Computer-Aided Detection  Second opinion
Purpose of CAD

Improvement of diagnostic accuracy
–Overload : 300 images/patient for lung CT
–Radiologist’s limitation : 45% sensitivity for 3mm nodule

Consistency of image interpretation
–Difficulty for radiologist to maintain high alertness at all time
CAD is best defined as a method of assisting radiologic interpretation by means of
computer image analysis. Ideally, CAD results in improved decision-making and performance
due to enhanced detection and evaluation of complex imaging features, decreased inter-observer
variability, and elimination of otherwise repetitive of tedious tasks.
Jane Ko, Naidich DP, “Computer-aided diagnosis and the evaluation of lung disease”. J Thorac
Imaging. 2004 Jul;19(3):136-55. Review.
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
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Effectiveness of CAD
To study the effects of CAD system
Large observer Test

22 lung nodules in 8 patients’ CT data sets

202 observers at a national radiology meeting
–68 nonradiologists, 95 nonthoracic radiologists, 39 thoracic radiologists

CAD system alone: 86.4% detection rate
Detection rates before and after CAD
Size of Nodule Nonradiologist Nonthoracic Radiologist Thoracic Radiologist
Before CAD After CAD Before CAD After CAD Before CAD After CAD
≤ 4mm 55.2 77.6 57.9 86.8 71.4 89.3
> 4mm 78.5 88.2 75.3 90.3 81.0 89.4
Matthew S. Brown, J.G. Goldin, “Computer-aided lung nodule detection in CT: results of large-scale
observer test”, Acad Radiol. 2005 Jun;12(6):681-6.
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
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Application of Various CAD
Breast cancer

Use of Digital Radiography
(Mammogram)

Fully commercialized: R2
(ImageChecker), etc…
Lung cancer

Use of developed CT technique

Commercialization is in progress by R2,
Siemens, Phillips
Colon and rectum cancer

Hot issue : began in 2000
Liver, Brain

Functional MRI, Neuroscience
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
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CAD for Lung Disease
2004 년 사망자중 사망원인별
2004 년 암에 의한 사망구성비
사망자수 ( 명 ) 구성비 (%) 1 일 평균 사망자 수 ( 명 )
암 65,000 26.3 177
뇌혈관질환 34,000 13.9 93
심장질환 18,000 7.3 49
고의적자해 ( 자살 ) 12,000 4.8 32
당뇨병 12,000 4.8 32
암사망자
구성비
폐암 위암 간암 대장암 췌장암 자궁암 전립선암 유방암 백혈병
1994 21.3 16.7 25.6 20.5 5.0 3.9 3.0 0.4 1.7 2.8
2004 26.3 20.6 17.4 16.9 9.1 4.7 2.1 1.4 2.3 1.7
증감 5.0 + 3.9 - 8.2 - 3.6 + 4.1 + 0.8 - 0.9 + 1.0 + 0.6 - 0.5
2004 년 사망원인 통계결과 – 통계청 (2005. 9. 발표 )
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
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What is characterization of SPN & GGO?
Solid Pulmonary Nodule Ground Glass Opacity
Shape
1. Small, round or egg-shaped lesion
2. Less than 3-4cm in diameter
1. Hazy lung opacity
2. May be seen as diffuse or more often
patchy in distribution taking sometimes a
geographic or mosaic pattern.
Importance
1. 40% of SPNs are malignant.
2. Malignant SPNs may be primary Stage I
lung cancer tumors or metastases from
other parts of body
1. Indicates the presence of an active and
potentially treatable process; active
disease is present in more than 80% of
patients who show GGO.
CAD
Research
1. Over 20 years research period
2. Good results with computing power and
new technology of multi-detector CT
image
1. More difficult than SPN because their
lower density
2. Early stage of research development
with texture analysis with neural network
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
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Previous Algorithm for SPN Detection
Research Group Sensitivity False positive Contents
Armato SG
(Med.Phys 2001)
70%
(89%)
1.5 /image
(1.3 /image)
9 features(2D, 3D)
First researth using 2.5 D
Linear discriminant analysis
Jane Ko
(Radiology 2001)
86% unspecified
location, shape, volume
based on time study
High detection error on vessel
Detection error large SPN
Brown MS
(Radiology 2002)
100%
70%
15 /case
3D shape information
High detection error on vessel
Detection error large SPN
Reeves AP
(SPIE 2004)
95.7% 19.3 / case
Only pleural nodule
3D search space, 3D connection
Not applied juxtavascular nodule
Lee JW
(Invest Radiol 2004)
81% 28.8 / case
3D recursive analysis
Use of Radial distribution
Performance dependency on location
Detection error large SPN
Practical Problems

High detection error with vessel component
–Single detection algorithm for all nodules

High false positive rate

Detection error with large solid pulmonary nodule
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
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Previous Algorithm for GGO Detection
Research Group Methods Results
Kim KG
Radiology. 2005
2D CT image processing
30 x 30 ROI (50% overlap)
2D texture feature
2 layer ANN
With a threshold of 0.9 (ANN)
Sensitivity: 94.3% (280/297 ROIs)
0.89 false positive /image
Display the square ROI box
Uchiyama Y
Med Phys 2003
For diffuse lung disease
32 x 32 (96 x 96) ROI analysis
3 layer ANN
Abnormal case
Sensitivity: 99.2% (122/123 ROIs)
Didn’t mention false positive rate
Not exact volume (only existence)
Kauczor HU
AJR 2000
Not given details
6% classified as GGO of total
2nd
classified 99% of GGO
False positive: 24%
Practical Problem

Segmentation exact area or volume of GGO

Input parameter dependency
–General 2D slice CT image & Texture only with ROI, Neural Networks (MLP)

High false positive rate

Algorithm for only pure GGO
1. Computer Aided Diagnosis
2. CAD for Lung Disease
3. Practical Problems
*ANN: Artificial Neural Network
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Contents
1. Introduction
2. Objective and Scope of Work
1. Objective and Scope of work
2. Summary of Proposal
3. Solid Pulmonary Nodule Detection Algorithm
1. Ground Glass Opacity Detection Algorithm
2. Further Study
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Objective & Scopes
Motivation

SPN detection
–Modular algorithm for each types of nodules
–Reduction of detection error for juxtavascular nodule

GGO detection
–3D CAD algorithm without artificial neural network
Objective
To find more powerful & New CAD algorithm
using 3D information of MDCT for early lung cancer
Scope of Work

Solid Pulmonary Nodule (SPN) Detection Algorithm

Ground Glass Opacity (GGO) Detection Algorithm
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Summary of Proposal
1. Computer Aided Diagnosis (CAD) is important to improve the accuracy and
consistency of radiologists in medical imaging.
2. CAD for lung nodule is significant. CAD process for lung nodule is consist of
automatic detection and classification.
3. For detection, proposed simple CAD algorithm showed a good preliminary
results with modular design according to each type of nodule.
4. Proposed GGO detection algorithm will show more efficient method than other
group.
Before Proposal Further Study
SPN
Detection
Algorithm(40%)
SPN CAD Evaluation (40%)
Verification of Algorithm(20%)
GGO
Detection
Algorithm (30%)
Supplement of Algorithm (20%)
GGO CAD Evaluation (45%)
Verification of Algorithm (5%)
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Contents
1. Introduction
2. Objective and Scope of Work
3. Solid Pulmonary Nodule Detection Algorithm
1. Introduction
2. Materials & Methods
1. SPN Detection algorithm development
2. 3D Morphological matching algorithm
3. Results
4. Conclusion
4. Ground Glass Opacity Detection Algorithm
5. Conclusion
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Introduction
Practical Problems

High detection error with vessel component
–Single detection algorithm for all nodules

High false positive rate

Detection error with large solid pulmonary nodule
Modular Detection Algorithm

Two detection CAD algorithm for 3 different types of SPN
–Isolated, pleural nodule – 3D shape analysis
–Juxtavascular nodule – 3D filter correlation method
3D image processing
3D pulmonary vessel extraction
3D morphological matching algorithm
1. Kim JS, Kim JH, Cho G, Bae KT. Automated detection of pulmonary nodules on CT images: effect
of section thickness and reconstruction interval--initial results. Radiology 2005; 236:295-299.
2. Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images
with morphologic matching algorithm--preliminary results. Radiology 2005; 236:286-293.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Proposed Algorithm (3 type Lung Nodule)
Isolated nodule Pleural nodule Juxtavascular nodule
3D morphological matching
method
Apply 3D shape & geometric
determinants
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Proposed Algorithm (Overall Diagram)
CT Image
Segmentation of
thorax & lung
Refine lung boundary
Segmentation of
lung structures
Generation of
3D lung data
3D lung data
Non-vessel group
containing
isolated, pleural
nodule
Vessel containing
juxtavascular
nodule
3DMM to segment
juxtavascular nodule
Apply shape &
geometric determinants
Nodule
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Method: Proposed Algorithm (Segmentation)
Segmentation process from original CT image to binary image. With morphological filter and image
processing algorithm, organs inside lung boundary were extracted. After this process 3D image
processing algorithm will be performed. After 2D Segmentation process, all binary images were stacked
to generate a 3D volumetric data which is shown right side.
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Method: Proposed Algorithm (Vessel grouping)
3D region growing,
labeling
Non-vessel group:
(Isolated, Pleural)
CT images
Segmentation of
2D lung region
Vessel group:
(Juxtavascular)
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Method: 3DMM Algorithm
3D shape features in non-vessel
group detection

Volume

Compactness

Elongation factor
3D Morphological Matching
method in vessel group

3D morphological filter
– spherical in shape, 4 sizes (3,6,9 and 12
mm in diameter)

The correlation between 3D data (I) and
3D shape filter (F)

Threshold values
–Threshold value was
determined empirically
at 70%
boxboundingtheofVolume
componentofVolume
=sCompactnes
33 <=
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lengthaxislong
Ef D
33
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cmVmm ππ <<
8.0,5.0 >< CorC
.)/)(2exp()()/2exp()(
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)()())((
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0
∑
∑
−
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−⋅⋅⋅=
−⋅=∗
n
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fffiii
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nxtiuuFnxiuuI
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txFtItFI
ππ
dataDeachinvoxelsofnumbernn
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3:,
:
:
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1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Method: MATLAB Coding
Main GUI program

Open files

DICOM viewer

2D segmentation

3D volume generation

3D labeling

Shape analysis
3DMM process

3D visualization

JPG export

Etc…
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Total 13 nodules
100% sensitivity
2 false positives
20 min processing time
Results: 3D visualization
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Results
Subject

20 patients (mean age 57 years), mean 265 images per patient

Multi-slice CT (Volume Zoom), 4x1 collimation, 0.5s scan, 120 KvP, 120 mAs

1 mm slice thickness, 1mm reconstruction intervals
Total 164 nodules (20 patients)

18 patients: 1-13 nodules (mean 4.7)

2 patients: 25, 54 nodules

# of nodules by size (diameter): 27 (>10mm), 80 (5-10mm), and 57 (3-5mm)
size location
Total
Diameter 3-5mm 5-10mm >10mm Isolated pleural vascular
Total nodules 57 80 27 78 52 34 164
CAD results 52 79 25 76 48 32 156
Sensitivity 91.2% 98.8% 92.6% 97.4 92.3 94.1 95.12%
Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching
algorithm--preliminary results. Radiology 2005; 236:286-293
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1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Conclusion
Performance of CAD Algorithm

20 patients, 164 solid pulmonary nodule

High sensitivity: average 95.12%
– Specially 94.1% for juxtavascular SPN

Low false positive rate: 4.0 per one patient

Modular design of 3D algorithm for types in terms of their proximity to
surrounding anatomic structure shows excellent performance.
Verification of CAD algorithm

Comparisons with other algorithm using common data is not completed.
– Lung image database consortium (LIDC, NIH)
: CT image acquisition are not taken in the whole lung region.
 CT Images of LIDC database are inadequate to apply our algorithm.
 No CAD research is done with LIDC database
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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Contents
1. Introduction
2. Objective and Scope of Work
3. Solid Pulmonary Nodule Detection Algorithm
4. Ground Glass Opacity Detection Algorithm
1. Introduction
2. Materials & Method
1. Subjects
2. GGO CAD Algorithm
3. Results
4. Conclusion
5. Conclusion
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Introduction: What is Ground Glass Opacity (GGO)?
I-ELCAP Definition

a CT finding of a partially-opaque region that does not obscure the structures contained within
Manifestations of lung cancer on CT

Solid nodule: most common

Nodule with GGO (part-solid nodules or nonsolid): 20%
–higher malignancy likelihood
Localized GGO

May not detected with most CAD system

Measurement of GGO’s volume is important for diagnosis
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Introduction: Previous GGO CAD algorithm

General 2D slice CT image & Texture only with ROI

Extraction GGO from complicated structures.

Classification tool: Neural Networks (MLP)
1. Kim KG, Goo JM, Kim JH, Lee HJ, Min BG, Bae KT, Im JG.
Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology. 2005 Nov;237(2):657-61.
2. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang CT, Suzuki K, Doi K. Quantitative computerized analysis of diffuse lung
disease in high-resolution computed tomography. Med Phys. 2003 Sep;30(9):2440
3. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, etc
Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison
with a density mask. Am J Roentgenol. 2000 Nov;175(5):1329-34.
4. International Conferences (SPIE, RSNA, CARS) posters and papers.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Introduction: Practical Problems  Solution
Segmentation of GGO

Volume is very important parameter for lung
cancer follow-up. 2D ROI based CAD can not
segment the exact area and calculate the
volume of GGO.
 Exact segmentation and volume measurement
of GGO using 3D image processing
Texture Parameter, ANN Dependency

Only gray value (HU) information

Kurtosis, surface curvature, inertia,
momentum, Entropy, energy, skewness, mean
etc…

6~96 features was used for analysis.

2~3 layer multi-layer perceptron, kNN training,
multiple neural network were used as
classification tool.

Classification GGO with other parameters
(volume, shape, simple texture) without ANN.
High False Positive rate

High false positive rate due to texture analysis
and ANN
Classification GGO with other parameters
(volume, shape, simple texture) without ANN.
Algorithm for only pure GGO

Single algorithm for pure GGO
SPN algorithm + GGO algorithm
= detection of mixed GGO.
2005 Radiology
Kim KG et al.
2006 Med Phys
Sluimer et al.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Introduction: Motivation
Practical Problem

Segmentation of exact area or volume of GGO

Input parameter dependency
–General 2D slice CT image & Texture only with ROI, Neural Networks (MLP)

High false positive rate

Algorithm for only pure GGO
3D GGO CAD Algorithm

3D image processing (segmentation, region growing, visualization) using MDCT
–3D cross mask processing

Rule based classification tool

Mixed GGO detection changing threshold value
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
The Purpose is…The Purpose is…
To develop an automatic CAD algorithmTo develop an automatic CAD algorithm
for Ground-Glass Opacity with Multi-Detector CTfor Ground-Glass Opacity with Multi-Detector CT
3. Kim JS, J.W Lee, J.H Kim, G. Cho, J.M Goo, Computer Aided Detection of Ground Glass Opacity
using Multi-Detector CT, (in preparation for submission to Korean Journal of Radiology)

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Method: Subject
Patients data

17 case (Male : 7, Female :10, mean age: 56)
– reviewed by two chest radiologists

Average 305 images/case (total 5186 images)

MDCT Image
–Seoul National University Hospital, ChungNam National University Hospital
–Sensation 16, Siemens, LightSpeed Ultra, GE, Mx8000, Philips
–120KVp, 120 effective mAs, 0.5s scan time
–Reconstruction interval, slice thickness: 1.00 ~ 1.25 mm
–512x512 16bits DICOM image
Ground Glass Opacity of Data
Size (mm) 3 ~ 5 6 ~ 10 11 ~ Total
Mixed GGO 1 2 9 12
Pure GGO 12 9 11 32
Total # of GGO 13 11 20 44
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: CT image including GGO
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Concept of GGO CAD
Flow diagram illustrating the overall scheme
for automated GGO detection from CT images.
Overall idea for GGO detection using MDCT images.
The main components in lung CT image of an
abnormal patient who has GGO were air, soft tissue,
GGO and some noises.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Decision of Threshold Value
-1024 -800 -700 -500 -400 -200
Initial range of GGO
threshold value
Optimal thereshold
value of GGO
Current standard of
threshold value of SPN
-800 HU ~ -200 HU -700 HU ~ -400 HU -500HU ~
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Segmentation
Segmentation process from original CT image to binary image. With morphological filter and image
processing algorithm, organs inside lung boundary were extracted. After this process 3D image
processing algorithm will be performed. After 2D Segmentation process, all binary images were stacked
to generate a 3D volumetric data which is shown right side.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: 3D Cross Mask
GGO candidate Extraction from 3D data set

3D Cross mask technique
–3D morphological filtering process

Ground glass opacity
–Defining GGO on the basis of shape is
difficult and subjective.
–But, they have fuzzy and star like shape.
We decide the optimal distance
between center and next GGO
candidate voxel as 3 pixels (1.5~2mm)
3D morphological process illustrating the 3D cross
mask. 3D morphological filtering process with 3D cross
mask initiates at the peaks in the marker volume and
spreads throughout the rest of the volume based on
the connectivity of the voxels.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Application of 3D Cross Mask
Before 3D cross mask process

Threshold value
: -700 HU ~ -400 HU
After 3D cross mask process

Threshold value
: -700 HU ~ -400 HU
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Method: Criteria after 3D Cross Mask
Classification criteria for GGO

Elongation factor

Volume
–GGO has some volume in 3 dimension.
–GGO larger than 3cm is meaningless.

Peak to Edge density ratio

3D profile
lengthshort axis
lengthaxislong
Ef D =3
lengthshort axis
lengthaxislong
Ef =sliceon2D
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
33
)3(
3
4
)3(
3
4
cmVmm ππ <<
peak
edgeedge

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Results: Overall Performance
GGO CAD Sensitivity
Processing Time

Pentium IV 3.0 GHz, RAM2.0 GByte

Microsoft Windows XP,

Processing time : (average) 110 seconds
False Positive

0.5 GGO/ case
Size (mm) 3 ~ 5 6 ~ 10 11 ~ Total
Mixed GGO
100%
(1/1)
100%
(2/2)
100%
(9/9)
100%
(12/12)
Pure GGO
50%
(6/12)
88.9%
(8/9)
90.9%
(10/11)
75%
(24/32)
Total # of GGO
53.8%
(7/13)
90.9%
(10/11)
95%
(19/20)
81.9%
(36/44)
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Results: CAD foundings (Pure GGO)
CAD found 6 mm size pure GGO
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
Introduction Objective & Scope SPN Detection ConclusionGGO Detection

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Results: Visualization GGO in volume
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion

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CAD found mixed GGO, 16 mm
Results: CAD foundings (Mixed GGO)
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Discussion & Conclusion
Ground-Glass Opacity can be detected from multi-slice CT
by the 3D cross mask and shape analysis algorithm
with high sensitivity and a relatively low false positives.

Segmentation and volume measurement of GGO is performed.

High performance without artificial neural network including detection of mixed GGO.

Our GGO CAD algorithm is focused on the low false positive rate (< 1 fp/case) than high
sensitivity. If we allow 4 fp/case, we can detect small GGOs (3~5mm) without problems.

Most of false negatives are less than 5mm.
–GGO smaller than 5mm is not important for lung disease.

With a large scale study, the performance, specially statistical analysis and clinical
application of the proposed GGO detection algorithm should be examined.
1. Introduction
2. Materials &
Methods
3. Results
4. Conclusion
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Contents
1. Introduction
2. Objective and Scope of Work
3. Solid Pulmonary Nodule Detection Algorithm
4. Ground Glass Opacity Detection Algorithm
5. Conclusion
1. Summary
2. Conclusion
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Summary for Lung Nodule CAD
Research Group Sensitivity False positive Contents
Armato SG
(Med.Phys 2001)
70%
(89%)
1.5 /image
(1.3 /image)
9 features(2D, 3D)
First researth using 2.5 D
Linear discriminant analysis
Jane Ko
(Radiology 2001)
86% unspecified
location, shape, volume
based on time study
High detection error on vessel
Detection error large SPN
Brown MS
(Radiology 2002)
100%
70%
15 /case
3D shape information
High detection error on vessel
Detection error large SPN
Reeves AP
(SPIE 2004)
95.7% 19.3 / case
Only pleural nodule
3D search space, 3D connection
Not applied juxtavascular nodule
Lee JW
(Invest Radiol 2004)
81% 28.8 / case
3D recursive analysis
Use of Radial distribution
Performance dependency on location
Detection error large SPN
Jin Sung Kim
(Radiology 2005)
95.12% 4 / case
3D morphological matching algorithm
High sensitivity on juxtavascular nodule, Low false
positive rate using modular algorithm on their type
Detection Error with Large SPN
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
Practical Problems were

High detection error with vessel component

High false positive rate

Detection error with large solid pulmonary nodule

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Summary for GGO CAD
Research
Group
Methods Results
Kim KG
Radiology. 2005
2D CT image processing
30 x 30 ROI (50% overlap)
2D texture feature
2 layer ANN
With a threshold of 0.9 (ANN)
Sensitivity: 94.3% (280/297 ROIs)
0.89 false positive /image
Display the square ROI box
Uchiyama Y
Med Phys 2003
For diffuse lung disease
32 x 32 (96 x 96) ROI analysis
12 feature, 3 layer ANN
Abnormal case
Sensitivity: 99.2% (122/123 ROIs)
Didn’t mention false positive rate
Not exact volume (only existence)
Kauczor HU,
Heitmann
AJR 2000,
Eur Radiol 1997
Not given details
ROI based texture analysis
Multiple neural network
6% classified as GGO of total
2nd
classified 99% of GGO
False positive: 24%
Jin Sung Kim
KJR 2006 preparing
3D ROI Selection
3D morphological Analysis
3D cross mask
Rule-based Analysis
Exact GGO segmentation
Volume information of GGO
Sensitivity: 81.9 %, 0.5 fp / case
Detection of mixed, pure GGO
Introduction Objective & Scope SPN Detection ConclusionGGO Detection
Practical Problems were

Segmentation of exact area or volume of GGO

Input parameter dependency, High false positive
rate,

Algorithm for only pure GGO

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Conclusion
New automatic computer aided detection (CAD) method was developed
for lung disease using multi-detector CT images.

Solid Pulmonary Nodule (SPN) Detection
–We developed an automated CAD program (3D morphological matching method) that takes
advantage of thin-section 3D volumetric data of MDCT images for pulmonary nodules based on 3
different types (isolated, juxtapleural, juxtavascular). The results of our study demonstrated that a
CAD system could detect nodules with high sensitivity and a relatively low false-positive detection
rate.

Ground Glass Opacity (GGO) Detection
–We developed a 3D CAD algorithm for localized GGO detection that apply ‘3D cross mask’ using
MDCT images without artificial neural network. The performance, such as exact GGO
segmentation, sensitivity and false positive rate of GGO CAD algorithm is superior to previous
studies.
Our developed CAD system may assist radiologists in the interpretation
of CT images, particularly for lung cancer screening.
Introduction Objective & Scope SPN Detection ConclusionGGO Detection

47. 감사합니다