2005 cad for ggo with svm
TRANSCRIPT
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Computer-Aided Diagnosis System for Ground-Glass Opacity
using MDCT ImagesJin Sung Kim, MS*, Jin-Hwan Kim, MD**, G. Cho, PhD*
Korea Advanced Institute of Science and Technology, Daejeon, Korea*, Department of Radiology, Chungnam National University Hospital, Daejeon, Korea**
2005 33th Korea Society of Medical & Biological Engineering, KINTEX
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Contents
• Introduction– What is CAD?– What is Ground Glass Opacity?– Previous GGO CAD algorithm– Purpose & Idea
• Methods– Concept overview– 3DMM algorithm– GGO Enhanced Image– Support Vector Machine
• Results• Conclusion
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Contents
• Introduction– What is CAD?– What is Ground Glass Opacity?– Previous GGO CAD algorithm– Purpose & Idea
• Methods– Concept overview– 3DMM algorithm– GGO Enhanced Image– Support Vector Machine
• Results• Conclusion
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What is CAD?
• What is CAD?– Computer-Aided Diagnosis– Computer-Aided Detection Second opinion
• Purpose of CAD– Improvement of diagnostic accuracy– Consistency of image interpretation
• CAD Application– Breast, Lung nodule, Polyp etc…
Introduction1. What is CAD?2. What is GGO?3. Previous GGO CAD4. Purpose & Idea
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Ground Glass OpacityIntroduction
I-ELCAP defined "ground-glass opacity" as a CT finding of a partially-opaque region that does not obscure the structures contained within (e.g. vessels).
1. What is CAD?2. What is GGO?3. Previous GGO CAD4. Purpose & Idea
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Previous GGO CAD
• 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.
• 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
• 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.
• International Conference (SPIE, RSNA, CARS)
Introduction1. What is CAD?2. What is GGO?3. Previous GGO CAD4. Purpose & Idea
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Purpose & Idea
• Previous GGO CAD research groups used– General 2D slice CT image & Texture only– Neural Networks (MLP)
• Our GGO algorithm proposes– Using 3D information with 3DMM algorithm – GGO Enhanced Image– Support Vector Machine
Introduction1. What is CAD?2. What is GGO?3. Previous GGO CAD4. Purpose & Idea
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Contents
• Introduction– What is CAD?– What is Ground Glass Opacity?– Previous GGO CAD algorithm– Purpose & Idea
• Methods– Concept overview– 3DMM algorithm– GGO Enhanced Image– Support Vector Machine
• Results• Conclusion
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Concept of GGO CAD
Air Component
Soft TissuePulmonary VesselSolid nodules
GGO nodules
CT Noises
After soft tissue & air component extraction, GGO detection is more easier !!!!.
Methods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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Overall AlgorithmMethods1. Concept overview
2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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3DMM Algorithm
• We proposed computer aided diagnosis (CAD) system for detection of solid pulmonary nodules using 3D morphological matching algorithm (3DMM) that takes advantage of 3D volumetric data.
• After 2D slice segmentation, extraction of pulmonary vessel is performed for isolated solid nodule detection.
“Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results”, Bae KT, Kim JS, Na YH, Kim KG, Kim JH, Radiology. 2005 Jul;236(1):286-93. “Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results”, Kim JS, Kim JH, Cho G, Bae KT, Radiology. 2005 Jul;236(1):295-9.
Methods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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SegmentationMethods1. Concept overview
2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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Vessel Extraction
3D image of segmented lung volume 3D image of extracted pulmonary vessel using 3D region-growing method
After vessel subtraction, 3D shape feature (volume, size, compactness, and elongation factor) were applied to non-vessel structure
from “Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results”, Bae KT, Kim JS, Na YH, Kim KG, Kim JH, Radiology. 2005 Jul;236(1):286-93.
Methods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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Original 2D Image Extracted Vessels
We can find a GGO in right lung region The GGO was not included in vessel
Methods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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GGO Enhanced ImageDetected Image using
thresholding technique
Original CT Image – Soft Tissue Image Using thresholding, GGO can be found
Methods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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Support Vector Machine• It is general that SVM shows better performance than
other Neural Network (MLP, etc…) in binary classification.
• OSU LIBSVM in MATLAB• Two independent set (total 29 cases)
– Training set(16), Test set(13)
• 10 input parameters• Kernel Type
– Polynomial, degree: 3
Materials & MethodsMethods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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Texture analysis
• 32x32 matrix
• Texture– Mean
– Standard deviation
– Skewness
– Kurtosis
– Area
– Compactness
– Eccentricity
Materials & MethodsMethods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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GGO CAD• Materials
– 120KVp, 120 effective mAs– 3.2 mm slice thickness – Average 126.9 images/patient
• ROI selection– 32x32 matrix in lung area
• Texture Analysis – Ave, std, kurtosis, skewness, etc…
• Classification– Support Vector Machine
Methods1. Concept overview2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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GGO CAD Program (MatLab)Methods1. Concept overview
2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine
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Contents
• Introduction– What is CAD?– What is Ground Glass Opacity?– Previous GGO CAD algorithm– Purpose & Idea
• Methods– Concept overview– 3DMM algorithm– GGO Enhanced Image– Support Vector Machine
• Results• Conclusion
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Detected GGO nodule with yellow box
Results
Overall sensitivity 84%(11/13) with 1.4 false-positive detections/study
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Detected GGO nodule with yellow box
Results
Sensitivity is depend on– SVM kernel type, SVM input parameters, etc…
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Contents
• Introduction– What is CAD?– What is Ground Glass Opacity?– Previous GGO CAD algorithm– Purpose & Idea
• Methods– Concept overview– 3DMM algorithm– GGO Enhanced Image– Support Vector Machine
• Results• Conclusion
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Conclusion
• In this paper, we proposed a novel diagnosis algorithm for GGO detection. Our CAD algorithm is a new & efficient for detection of GGO nodules using 3D morphologic features, 2D texture analysis and support vector machine learning method.
• Enhanced GGO Image and support vector machine is good combination for GGO detection.
• With more patients and performance evaluation of SVM classifier, our CAD system will be improved.
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감사합니다 감사합니다 !!!!