2005 CAD for GGO with SVM

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

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

Contents




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

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

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)


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


Contents




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

Overall AlgorithmMethods1. Concept overview

2. 3DMM Algorithm3. GGO Enhanced Image4. Support Vector Machine

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.


SegmentationMethods1. Concept overview


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.


Original 2D Image Extracted Vessels

We can find a GGO in right lung region The GGO was not included in vessel


GGO Enhanced ImageDetected Image using

thresholding technique

Original CT Image – Soft Tissue Image Using thresholding, GGO can be found


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

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

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


GGO CAD Program (MatLab)Methods1. Concept overview


Contents




Detected GGO nodule with yellow box

Results

Overall sensitivity 84%(11/13) with 1.4 false-positive detections/study

Detected GGO nodule with yellow box

Results

Sensitivity is depend on– SVM kernel type, SVM input parameters, etc…

Contents




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.

감사합니다 감사합니다 !!!!

Health & Medicine

2005 CAD for GGO with SVM