20050831#lab conference#김진성

Computer-Aided Diagnosis System for Ground-Glass Opacity

using MDCT Images

2005. 8. 31 Jin Sung Kim

2005 RDMI Lab Conference

Contents

• Introduction– Ground Glass Opacity– Purpose & Idea

• Methods– Concept of Algorithm– Image Processing Module– Texture Analysis– Support Vector Machine

• Further Study


Ground Glass OpacityIntroduction


Introduction

• Focal ground-glass opacity (GGO) is a finding of early adenocarcinoma or its precursor

• Ground-glass opacity (GGO) detection becomes more simple & efficient after extraction of vessels & solid nodules using 3DMM algorithm

• In this exhibition, I will describe our automated GGO nodule detection program that takes advantages of 3D volumetric data from multi-slice CT

• Japan-Korea Joint Symposium on Medical ImagingJapan-Korea Joint Symposium on Medical Imaging

2005. 9.21~22. 서울 고려대학교 구로병원

Introduction


Purpose & Idea

• Previous research groups– General 2D slice CT image– Neural Networks (MLP)

• This Study– 3DMM algorithm – GGO Enhanced Image– Support Vector Machine

Introduction


Material

• 10 patients have GGO nodule

• 120KVp, 120 effective mAs

• 3.2 mm slice thickness

• Average 126.9 images/patient

• Programming based on Matlab

• OSU LIBSVM in matlab

Materials & Methods


Methods

Air Component

Soft TissuePulmonary VesselSolid nodules

GGO nodules

CT Noises

After soft tissue & air component extraction, GGO detection is more easier !!!!.

IntroductionMaterials & Methods


Overall AlgorithmIntroductionMaterials & Methods


3D Volume of segmented lung regionMethodsMaterials & Methods


3D Image of Pulmonary Vessel extraction using 3DMM algorithm

MethodsMaterials & Methods

The GGO was not include in vessel

We can find a GGO in right lung region

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


ROI matrix, texture analysis

• 32x32 matrix

• Texture– Mean– Standard

deviation– Skewness– Kurtosis– Area– Compactness– Eccentricity– Etc…

Materials & Methods


1. Final Extraction Image


2. GGO Enhanced Image


3. Original CT Image


Texture AnalysisMaterials & Methods


Support Vector Machine

• 11 parameters, 29 cases• Using OSU LIBSVM in matlab• Kernel Type– Polynomial, degree:5

• [AlphaY, SVs, Bias, Parameters, nSV, nLabel]= u_PolySVC(T_Samples, T_Labels, Degree);

• [Labels, DecisionValue]= SVMClass(T_Test, AlphaY, SVs, Bias, Parameters, nSV, nLabel);

• Result– Label : [0 0 1 1]– DecisionValue

Materials & Methods


Results

• Image processing

• Texture analysis

completed!

• SVM classification

진행중 ...

• Final Results


Further Study

• SVM Training, Testing– 많은 Case, – Training set 과는 다른 Test set 적용

• 통계처리– Sensitivity, Specificity, – ROC curve analysis

• GUI development

Thank you!!!

20050831#lab conference#김진성

Education