Computer Aided Diagnosis using

Support Vector Machine

Jin Sung Kim

Radiation Detection & Medical Imaging LabLab Seminar, 2005. 6. 2.

Contents

• Introduction Computer Aided Diagnosis (CAD) CAD in process

• Support Vector Machine What is SVM

– Basic idea, Why OHP

Non-linear Separable Case– Non-Linear SVM

Multi-class Classification Application in CAD

• Conclusion

• Further Study

• Reference2

Contents

• Introduction Computer Aided Diagnosis (CAD) CAD in process

• Support Vector Machine What is SVM

– Basic idea, Why OHP

Non-linear Separable Case– Non-Linear SVM

Multi-class Classification Application in CAD

• Conclusion

• Further Study

• Reference

Computer-Aided Diagnosis

• What is CAD?Computer-Aided DiagnosisComputer-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

Introduction

3

CAD Application

• Breast cancerFully commercialized : ImageChecker, etc…

• Lung cancerCommercialization is in progress by R2, Siemens,

Phillips

• Colon and rectum cancerbegan in 2000

• Liver, brain, etc…

Introduction

4

CAD in Process

• Lung Nodule CharacteristicDetection process was done.

2D, 3D image processing with Multi-detector CT.

Need to Classify nodule as Benign or Malignant.

• GGO (Ground Glass Opacity)ROI based 2D image processing.Need to Classify candidates as GGO or Nodule

We need a good classification toolgood classification tool !!!

Introduction

5

Classification Tool

• What is “good” classification tool?Good Performance!!

High sensitivity, low error rate

Non-linear!! Input parameters were complicated

Using learning theory Case by case

Artificial Neural Network MultiLayer Perceptron (MLP) RBFN (Radial-Basis Function Network) SVM (Support Vector Machine)

Introduction

6

Contents

• Introduction Computer Aided Diagnosis CAD in process

• Support Vector Machine What is SVM

– Basic idea, Why OHP

Non-linear Separable Case– Non-Linear SVM

Multi-class Classification Application in CAD

• Conclusion

• Further Study

• Reference

Support Vector Machine

SVMInvented by Vapnik, 1995

Simple, and always trained to find global optimum

Used for pattern recognition, regression, and linear operator inversion

Considered too slow at the beginning, but now for most application this problem is overcome due to late 1990’s

Small number of parameters choice – easy to use

Support Vector Machine

7

Basic Idea

length

weight Optimal Hyperplane (OHP)

simple kind of SVM (called an LSVM)

margin

Support vectorsmaximum

margin

Ph.DMaster

Support Vector Machine

8

Higher Dimensional Space

Mapping into higher dimensional space,

then find minimum ||W ||2 in that spacefeature space

weight2

length2

weight * length

Hypersurface

lengthsd

Kernalization

Hyperplane

Original Data

Support Vector Machine

9

Multi-class Classification

Using Multi-class SVM

Using two-class SVMOne-against-othersOne-against-one

Other variations

Support Vector Machine

10

One-against-others method

Using N classifiers

-versus

+Classifier 1

versusClassifier 2

versusClassifier 3

uXUnseen data

Classifier 2

Classifier 3

Classifier 1 Sign + , - ?

More than one classifier can generate +All classifier can generate -

Easy and simple

variation

Support Vector Machine

11

One-against-one method

Using NC2 classifiers

versus+ -

Classifier 1

versusClassifier 2

versusClassifier 3

Unseen data

Classifier 1

Classifier 2

Classifier 3

uX

Sign + , - ?

Too many classifierComplicate and more time required

More accurate than one-against-one

variation

Support Vector Machine

12

SVM Tools

• Based on Matlab platformSVMlight software

http://svmlight.joachims.orgMost common, powerful tool Simple and easy to useSome example in “Next Seminar”

http://svm.dcs.rhbnc.ac.uk/

Support Vector Machine

13

Application• The SVM (training and testing) was trained with the

extracted features using SVM tools with Matlab.

Skewness

Standard Deviation

Kurtosis

HU Average

Histogram

Input parameter SVM

Methods

GGO

vs

Not GGO

Benign

vs

Malignant

Support Vector Machine

14

Contents

• Introduction Computer Aided Diagnosis Objective

• Support Vector Machine What is SVM

– Basic idea, Why OHP

Non-linear Separable Case– Non-Linear SVM

Multi-class Classification Application in CAD

• Conclusion

• Further Study

• Reference

Conclusion

• Computer aided detection was performed with lung disease.

(Nodule, GGO)• We need a good classification tool for

diagnosis as second opinion.• SVM is simple, and always trained to find

global optimum for classification.• With SVM, CAD will present better

performance for diagnostic opinion.

Conclusion

15

Contents

• Introduction Computer Aided Diagnosis Objective

• Support Vector Machine What is SVM

– Basic idea, Why OHP

Non-linear Separable Case– Non-Linear SVM

Multi-class Classification Application in CAD

• Conclusion

• Further Study

• Reference

Further Study

• SVM development in matlab

• Classification of Nodule CharacteristicsAutomatic extraction & detectionUse the HU, shape of nodule in ROI

• Classification of GGO noduleUse many 2D textures analysis

• Result in 2 month (I hope)16

Reference

“An Introduction to Lagrange Multipl iers”, Steuard Jensen http://home.uchicago.edu/~sbjensen/Tutorials/Lagrange.html

“Linear Algebra and Its Applicat ions,” David C. Lay, 1999, second edit ion “Some Mathematical Tools for Machine Learning,” Chris Burges,

August, 2003 “Statist ical Learning and VC Theory,” Peter Bartlett, ISCAS, May

2001 “A Tutorial on Support Vector Machines for Pattern Recognit ion,”

Christopher J.C. Burges, Data Mining and Knowledge Discover, 1998

“Support Vector Learning,” B. Schölkopf, Ph. D. Thesis, 1997 “Kernel methods: a survey of current techniques,” Colin Campbell, 2002

Reference

Thank you for your attention!