The document discusses Insight Toolkit (ITK), an open-source software library for image analysis. ITK can be used for tasks like image segmentation, registration, and processing. It has algorithms for segmentation like watersheds, level sets, and thresholding. The document then describes using ITK to develop a system for detecting lung nodules in 3D chest CT scans. The system performs lung segmentation, finds nodule candidates, extracts features, and aims to classify nodules versus false positives using methods like linear discriminant analysis. Experimental data comes from the LIDC lung cancer CT scan database.

1. Insight Toolkit 을 이용한 삼차원 흉부
CT 영상분석 및 폐 결절 검출 시스템
광주과학기술원 기전공학부
최욱진

2. Insight Toolkit (ITK)
• www.itk.org
• 2000 년 부터 개발
• Image Processing Toolkit
– C++ 라이브러리 (+2 million LOC)
– Java, Python, TCL 등의 언어 지원
– Linux, Windows, Mac OSX, Solaris 등 다양한
운영체제에서 사용가능
• Very active community: 1500+ registered
users
2014. 4. 21. 2

3. ITK
• Visible human 데이터를
처리하기 위해서 개발 되었음
• 영상처리 라이브러리
• Segmentation
• Registration
• GUI를 제공하지 않음
• Visualization 기능 없음
– Visualization Toolkit (VTK)
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4. ITK
• Image IO
– PNG, JPEG, DICOM 등의 IO처리
– 확장 가능한 구조
• Numerics
– Vision Numerics Library (VNL) 기반
– Matrix, Vector 처리
• N-dimensional
– 1D : ultrasound
– 2D : X-ray
– 3D : CT, MRI
– 4D : 3D + time, e.g. Beating heart MRI
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5. ITK programing model: Pipeline
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Reader Image
Parameter
File
Filter
File Writer Object

6. ITK programing model: Pipeline
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Threshold Filter Example

7. 기본 영상처리 Filter
• Denoising
• Image enhancement
• Edge detection
• Intensity filtering
• …
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8. Image Segmentation
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9. Image Segmentation
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10. Segmentation 알고리즘
• Region Growing
– Confidence Connected
– Connected Threshold
– Isolated Connected
• Watersheds
• Level Sets
– Fast Marching
– Shape Detection
– Geodesic Active Contours
– Threshold Segmentation
– Canny Segmentation Level Set
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11. Shape Detection
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Gradient Sigmoid
Gradient
Magnitude
Smooth Rescale
Shape
Detection
Threshold
Input
Image
Binary
Mask
Positive
Level Set
Feature
Image
Balanced
[-0.5,0.5]
Input
Feature
Input
Level Set
Output
Level Set

12. Registration
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13. Image Registration Framework
Fixed
Image
Transform
Moving
Image
Optimizer
Metric + Domain
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Parameters

14. Registration 알고리즘
• Transforms
– Linear: Translation, Rigid, Affine
– Deformable: B-Spline, TP-Spline, Daemon
• Metrics
– Mean square
– Cross-correlation
– Mutual Information
– …
• Optimizers
– Gradient Descent
– Genetic algorithms
– Conjugate gradient
– …
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15. Satellite Images
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16. ITK Application 개발
C++ Glue code
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ITK
Image
Processing
GUI
MFC, QT,
wxWindows,
FLTK
Visualization
OpenGL, VTK

17. ITK applications
• Slicer
• Osirix
• MeVisLab
• XIP
• …
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18. Slicer
• The Slicer Community 에서 개발
하는 오픈소스 소프트웨어
• Visualization과 Image analysis
• Windows, Linux, OSX에서 동작
• 주요기능
– Robust DICOM Capabilities
– Interactive Segmentation
– Volume Rendering
– Rigid and Nonrigid Registration
– SceneSnapshot Screen Capture
Functionality
– 4D Image Viewer
– Flexible Layouts and Slice Viewers
– Extension Manager for exploring
and installing plug-ins
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19. OsiriX
• OsiriX foundation(오픈소스),
Pixmeo(상용제품)
• OSX전용 DICOM viewer
• TIFF (8,16, 32 bits), JPEG, PDF, AVI, MPEG
and QuickTime
• Fully compliant with the DICOM standard
• DICOM communication protocol
– Any PACS or medical imaging modality
– STORE SCP - Service Class Provider, STORE
SCU - Service Class User, and
Query/Retrieve
• OsiriX MD
– Cleared by the FDA, as a Class II Medical
Device
– Certified as an European CE Class IIa, for
diagnostic imaging in medicine
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OsiriX
Foundation

20. MeVisLab
• MeVis Medical Solutions AG
와 Fraunhofer MEVIS 개발
• A cross-platform application
framework
– medical image processing
– scientific visualization
• 주요기능
– Image registration
– Segmentation
– Quantitative morphological
and functional image analysis
– IDE
• Graphical programming
• Rapid user interface
prototyping
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21. XIP
• Siemens 와 Washington University 개발
• Open Source framework and platform
– Rapidly developing Medical Imaging
applications
– Multiple computing environment
• Siemens
– DICOM data handling and navigation
– Overlay and region of interest (ROI) objects
– ITK and VTK support with automatic Inventor
wrapper generation
– XIP Builder: a Visual Programming interface to
design XIP scenegraph
• Washington University
– Remotely hosted grid based components and
data sources (available data sources include
NCIA)
– Components for CAD algorithms
– Components for quantifying changes across
time in consecutive imaging studies
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22. 폐 결절 검출 CAD 시스템
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23. 영상관리시스템
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24. 영상 뷰어
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25. ITK를 이용한 폐 결절 검출 시스템
개발
C++ Glue code
Java GUI
Java Swing
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VTK
ITK
Image
Processing
GUI
MFC, QT,
wxWindows,
FLTK
Visualization
OpenGL, VTK

26. Java와 C++ 비교
C++
• 장점
– ITK가 C++로 개발되어
모든기능을 사용가능
– 실행 속도가 빠름
– OS 고유기능 사용 가능
• 단점
– 문법이 복잡하여 접근성이
떨어짐
– 개발 속도가 느림
– 멀티플랫폼 개발 어려움
Java
• 장점
– 비교적 단순한 문법으로
접근성이 좋음
– 안정적임
– 멀티플랫폼 개발 용이
– 개발 속도가 빠름
• 단점
– ITK의 binding지원이 완벽하지
않아서 일부 기능 사용 불가능
• 거의 대부분의 기능 사용 가능
– 속도가 느림
• 빠른 속도가 필요한 영상처리
부분은 ITK를 이용하여 해결
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27. 폐 결절 검출 Pipeline
3D Lung
Image
itkImageSeriesReader
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Meta Data
DICOM
Data
3D Lung
Mask
Lung Volume
Segmentation
Nodule Candidates
Detection
Nodule
Candidates
Label Map
False Positive
Reduction
Nodules
Label Map

28. LUNG VOLUME SEGMENTATION
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29. Lung Volume Segmentation
Pipeline
Lung
Label Map
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3D Lung
Image
ItkThreshold Filter
Parameter
3D Lung
Mask
Remove Rim
Refine Lung Mask
Extract Lung
Volume
itkBinaryToShapeLa
belMapFilter
itkLabelMapToBinaryI
mageFilter

30. 폐 영상 Threshold
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31. 폐 영역 추출
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32. Rim 제거
3D to 2D
2D 필터처리
2D to 3D
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33. Rim 제거: 2D 영상처리
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34. Rim 제거
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35. 폐 영역 추출
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36. Lung Mask 생성
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37. Lung Mask 생성
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38. Segmented Lung Volume
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39. NODULE CANDIDATES DETECTION
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40. Nodule Candidates Detection
Pipeline
itkBinaryToShapeLabel
MapFilter
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3D Lung
Image
ItkMaskImageFilter
3D Lung
Mask
Nodule
Candidates
Label Map
Multi Thresholds Detection
ItkThresholdFilter
Rule Based Filtering
itkLabelMapToBinaryI
mageFilter
Nodule
Candidates
Masks
itkOrImageFilter

41. Rule-based Filtering
• Rule-based filtering을 통해 폐 혈관과 noise
제거
• 혈관 제거
– Volume is extremely bigger than nodule
– Elongated object
• Noise 제거
– Radius of ROI is smaller than 3mm
– Bigger than 30mm
• Remaining ROIs are nodule candidates
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42. Rule-based Filtering
Rule Description
R1 Small noise
R2 Vessel
R3 Large noise
R4 Nodule
42
Rules for nodule candidate detection
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43. itkBinaryToShapeLabelMapFilter
Label Map
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Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Shape
Label
Object
Shape
Label
Object
Binary
Image
itkBinaryToShapeLabelMapFilter
결절 후보들

44. itkShapeLabelObject
Shape Features
1. Bounding Box
2. Centroid
3. Elongation
4. Equivalent Ellipsoid Diameter
5. Equivalent Spherical Perimeter
6. Equivalent Spherical Radius
7. Feret Diameter
8. Flatness
9. Number Of Pixels
10. Number Of Pixels On Border
11. Perimeter
12. Perimeter On Border
13. Perimeter On Border Ratio
14. Physical Size
15. Principal Axes
16. Principal Moments
17. Roundness
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itkShapeLabel
Object

45. Multi Thresholds Detection
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46. Rule-based Filtering
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47. Multi Thresholds Detection (cont.)
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48. FALSE POSITIVE REDUCTION
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49. False Positive Reduction
• 검출된 결절 후보에서 결절이 아닌 것을 제거
하고 결절을 찾는 과정
– 많은 False Positive 가 포함되어 있음
• 결절 후보에서 feature(특징 값) 추출
• Feature 데이터를 이용하여 Classification
– Rule-based Classifier
– Linear Discriminant Classifier
– 머신러닝 기반의 classifier
• Artificial Neural Network, Genetic programming, Support
Vector Machine
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50. Feature Selection
50
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
2 f
3 f
4 f
5 f
6 f
7 9 f ~ f
10 12 f ~ f
13 f
14 f
15 f
16f
17f
18f
19f
20 27 f ~ f
28 f
29 f
30 f
31 f
32 f
33 f
Features for nodule detection
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51. itkBinaryToStatisticsLabelMapFilter
Label Map
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Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Statistics
Label
Object
Binary
Image
itkBinaryToStatisticsLabelMapFilter
결절 후보들

52. itkStatisticsLabelObject
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Statistics Features
1. Center Of Gravity
2. Histogram
3. Kurtosis
4. Maximum
5. Maximum Index
6. Mean
7. Median
8. Minimum
9. Minimum Index
10. Skewness
11. Standard Deviation
12. Sum
13. Variance
14. Weighted Elongation
15. Weighted Flatness
16. Weighted Principal Axes
17. Weighted Principal Moments
Shape Features
1. Bounding Box
2. Centroid
3. Elongation
4. Equivalent Ellipsoid Diameter
5. Equivalent Spherical Perimeter
6. Equivalent Spherical Radius
7. Feret Diameter
8. Flatness
9. Number Of Pixels
10. Number Of Pixels On Border
11. Perimeter
12. Perimeter On Border
13. Perimeter On Border Ratio
14. Physical Size
15. Principal Axes
16. Principal Moments
17. Roundness
itkStatisticsLabel
Object
itkShapeLabel
Object

53. Classification (개발중)
• WEKA
– A collection of open
source ML algorithms
• pre-processing
• classifiers
• clustering
• association rule
– Created by researchers
at the University of
Waikato in New Zealand
– Java based
2014. 4. 21. 53

54. 실험 데이터
• 미국 National Cancer Institute (NCI)의 LIDC database 사용
– 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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55. 실험 결과
135초
120초
24초
80초
160
140
120
100
80
60
40
20
0
Lung Segmenation Nodule Candidate Detection
기존 시스템 ITK 기반 시스템
2014. 4. 21. 55
평균 실행시간

56. 실험 결과
기존 시스템
• 단일 쓰레드 프로그램
• MATLAB, C++
• 영상 크기가 크면 속도
저하 심함
• 사용이 불편함
ITK 기반 시스템
• 멀티 쓰레드 프로그램
• Java, ITK, VTK
• 안정적으로 동작
• 처리속도 빠름
• 사용이 편함
2014. 4. 21. 56

57. 결론
• Insight Toolkit (ITK)
– 의료영상처리를 위한 라이브러리
– 다양한 영상처리 알고리즘 제공
– DICOM 및 다양한 영상 데이터 처리가능
• ITK를 이용하는 Applications
– Slicer
– Osirix
– MeVisLab
– XIP
– ...
2014. 4. 21. 57

58. 결론
• ITK기반 폐결절 검출 시스템 개발
– Java
• 빠른 개발
• 안정성 및 확장가능성 높임
– 영상 처리 속도 개선
• 향후 계획
– Weka를 이용한 False Positive Reduction 기능 개발
(진행중)
– Java Swing + VTK 기반의 GUI 및 Visualization 기능
– 호환성 및 사용 편의성 증대
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