Eric K. T. Hui University of Waterloo, M.A.Sc. Seminar Wednesday, November 12, 2003

Region-Based Feature Extraction of Region-Based Feature Extraction of Prostate Ultrasound Images: Prostate Ultrasound Images:

A Knowledge-Based ApproachA Knowledge-Based ApproachUsing Fuzzy InferencingUsing Fuzzy Inferencing

Eric K. T. HuiEric K. T. HuiUniversity of Waterloo, M.A.Sc. SeminarUniversity of Waterloo, M.A.Sc. Seminar

Wednesday, November 12, 2003Wednesday, November 12, 2003

4:30 PM in DC 25844:30 PM in DC 2584

2

OutlineOutline IntroductionIntroduction Medical BackgroundMedical Background Related ResearchesRelated Researches Problem FormulationProblem Formulation Proposed Feature ExtractionProposed Feature Extraction AnalysisAnalysis ConclusionsConclusions Future WorksFuture Works Questions and CommentsQuestions and Comments

3

IntroductionIntroduction- Prostate Cancer -- Prostate Cancer -

Prostate cancerProstate cancer is the most is the most frequently diagnosed cancer in frequently diagnosed cancer in Canadian men:Canadian men: 18,800 will be newly diagnosed.18,800 will be newly diagnosed. 4,200 will die of it.4,200 will die of it.

Exact cause remains unknown.Exact cause remains unknown. Early detection is the key in Early detection is the key in

controlling and localizing cancerous controlling and localizing cancerous cells.cells.

4

IntroductionIntroduction- TRUS -- TRUS -

Digital Digital transrectal ultrasonography transrectal ultrasonography (TRUS)(TRUS) One of the early detection techniques.One of the early detection techniques. Low cost, high availability, high safety, Low cost, high availability, high safety,

immediate results.immediate results. TRUS can be used to plan and guide TRUS can be used to plan and guide

prostate biopsy.prostate biopsy. This thesis tries to automate the This thesis tries to automate the

cancerous region detectioncancerous region detection process. process.

5

IntroductionIntroduction- Features -- Features -

FeatureFeature:: Measurement of some characteristics Measurement of some characteristics

(e.g. darkness, texture).(e.g. darkness, texture). A good feature should be A good feature should be

discriminativediscriminative so that, ideally, the so that, ideally, the cancerous regions are mapped to a cancerous regions are mapped to a different range of different range of feature valuesfeature values in in the the feature spacefeature space than the non- than the non-cancerous regions.cancerous regions.

feature valuecancerousbenign

6

IntroductionIntroduction- This Thesis -- This Thesis -

This thesis proposes a new feature This thesis proposes a new feature extraction method:extraction method: Spatial locationSpatial location, , symmetrysymmetry, and other , and other

geometric measurements of the geometric measurements of the regions-of-interestregions-of-interest, in addition to the , in addition to the greylevel and texture.greylevel and texture.

Uses a semi-automatic Uses a semi-automatic fuzzy inferencing fuzzy inferencing system (FIS)system (FIS) to relate all the features to relate all the features and mimic radiologists’ and mimic radiologists’ knowledgeknowledge..

Outline

7

Medical BackgroundMedical Background- Male Reproductive System - Male Reproductive System

--

penis

bladder

prostate gland

testis

urethra

uretervas deferens

seminal vesicles

bulbourethral gland

ejaculatory ducts

rectum

8

Medical BackgroundMedical Background- Prostate Zonal Anatomy -- Prostate Zonal Anatomy -

central zone (CZ)

peripheral zone (PZ)

anterior fibromuscular stroma (AFMS)

ejaculatory duct

bladder

transition zone (TZ)

rectum

seminal vesicles

vas deferens

urethra

verumontanum

9

Medical BackgroundMedical Background- BPH -- BPH -

Young and healthy Young and healthy prostate:prostate:

Prostate withProstate with Benign Benign prostatic hyperplasia prostatic hyperplasia (BPH)(BPH)::

TZ

AFMS

CZ

PZ

ejaculatory ducts

urethra

TZ

CZ

PZ

ejaculatory ducts

urethra

TZ

AFMS CZ

PZ

ejaculatory duct

urethra

CC’

TZ

AFMS CZ

PZ

ejaculatory duct

urethra

C’C

Back

10

Medical BackgroundMedical Background- Prostate Cancer -- Prostate Cancer -

Prostate cancerProstate cancer involves the growth involves the growth of malignant prostate tumours and of malignant prostate tumours and can be life threatening.can be life threatening. Uneven statistical distributionUneven statistical distribution::

70% originates in 70% originates in PZPZ.. 10% originates in 10% originates in CZCZ.. 20% originates in 20% originates in TZTZ..

Cancer tends to be localized in the early Cancer tends to be localized in the early stage, any stage, any asymmetryasymmetry on the axial view on the axial view might suggest cancer development.might suggest cancer development.

TZ

CZ

PZ

ejaculatory ducts

urethra

11

Medical BackgroundMedical Background- TRUS Imaging -- TRUS Imaging -

Echoicities:Echoicities:

hyperechoic

hypoechoic

isoechoic

… + anechoic

12

Medical BackgroundMedical Background- TRUS Imaging -- TRUS Imaging -

TRUS imaging:TRUS imaging: About 80% of prostate About 80% of prostate cancer tissuescancer tissues

consist of consist of hypoechoichypoechoic tissues (mixed tissues (mixed with other echoicities).with other echoicities).

Different Different probesprobes (e.g. end-fire, side-fire) (e.g. end-fire, side-fire) give different give different shapesshapes of the captured of the captured image of the prostate.image of the prostate.

Image

13

Medical BackgroundMedical Background- Summary -- Summary -

Uneven cancer statistical Uneven cancer statistical distribution.distribution.

Asymmetry of regions-of-interest.Asymmetry of regions-of-interest. TRUS echoicities.TRUS echoicities. Different probes give different Different probes give different

prostate shapes.prostate shapes.

Outline

14

Related ResearchesRelated Researches

Transform-BasedTransform-Based Fourier TransformFourier Transform Gabor TransformGabor Transform Wavelet TransformWavelet Transform

Statistic-BasedStatistic-Based First-Order StatistiFirst-Order Statisti

cscs Second-Order StatiSecond-Order Stati

sticsstics

15

Related ResearchesRelated Researches- Fourier Transform -- Fourier Transform -

Fourier Transform:Fourier Transform: Decompose into pure frequencies:Decompose into pure frequencies:

Not localized in spatial domain.Not localized in spatial domain. A global operator.A global operator.

dxexfuF uxj 2)()(

Chapter Outline

16

Related ResearchesRelated Researches- Gabor Transform -- Gabor Transform -

Gabor Transform:Gabor Transform: ““Windowed Fourier Transform”.Windowed Fourier Transform”.

Trade off between spatial and frequency Trade off between spatial and frequency resolutions.resolutions.

dxekxwxfukF uxjGT

2* )()(),(

)(xf

x

)(uF

u0

Sp

ati

al

Dom

ain

Fre

qu

en

cy

Dom

ain

)(xf

x

)(uF

u0

17


Gabor Filter:Gabor Filter: A variation of the Gabor Transform.A variation of the Gabor Transform. Translate the window in the frequency Translate the window in the frequency

domain to capture different frequency domain to capture different frequency components.components.

18


Gabor Filter:Gabor Filter: It’s It’s anisotropicanisotropic (i.e. orientation (i.e. orientation

dependent).dependent).

path

of

ultr

asou

nd w

ave

texture orientation

Chapter Outline

19

Related ResearchesRelated Researches- Wavelet Transform -- Wavelet Transform -

Wavelet Transform:Wavelet Transform: Multiresolution Analysis (MRA).Multiresolution Analysis (MRA). Different dilations of basis functions to Different dilations of basis functions to

analyze different scales.analyze different scales.

h ↓2

gH

gV

gD

↓2

↓2

↓2

h ↓2

gH

gV

gD

↓2

↓2

↓2

h ↓2

gH

gV

gD

↓2

↓2

↓2h ↓2

gH

gV

gD

↓2

↓2

↓2

h ↓2

gH

gV

gD

↓2

↓2

↓2

h ↓2

gH

gV

gD

↓2

↓2

↓2

20

Related ResearchesRelated Researches- Transform-Based - Transform-Based

Limitations -Limitations - Limitations of transform-based Limitations of transform-based

methods:methods: Similar frequency spectrum.Similar frequency spectrum.

0 10 20 30 40 50 60 70 800

500

1000

1500

2000

2500

3000frqGT(u)

u

)(uF

0 10 20 30 40 50 60 70 8040

60

80

100

120

140

160vtrOrig

x

)(xf

Sp

ati

al

Dom

ain

Fre

qu

en

cy

Dom

ain

Chapter Outline

21

Related ResearchesRelated Researches- First-Order Statistics -- First-Order Statistics -

First-Order Statistics:First-Order Statistics: Greylevel of each pixel.Greylevel of each pixel. One of the most discriminative features.One of the most discriminative features.

Chapter Outline

Cancerous RegionTRUS Image

22

Related ResearchesRelated Researches- Second-Order Statistics -- Second-Order Statistics -

Second-Order Statistics:Second-Order Statistics: Statistics on two neighbouring pixels.Statistics on two neighbouring pixels. Requires a Requires a windowwindow defining the defining the

neighbourhood.neighbourhood. Greylevel Difference Matrix (GLDM):Greylevel Difference Matrix (GLDM):

Contrast (CON):Contrast (CON): Mean (MEAN):Mean (MEAN): Entropy (ENT):Entropy (ENT): Inverse Difference Moment (IDM):Inverse Difference Moment (IDM): Angular Second Moment (ASM):Angular Second Moment (ASM):

)|(2 dipifi

contrast )|( dipif

imean

i

entropy dipdipf )|(log)|(

iidm i

dipf

)1(

)|(2

i

asm dipf 2)|(

Back

23

Related ResearchesRelated Researches- Summary -- Summary -

All these methods were successfully All these methods were successfully applied to extract features from:applied to extract features from: Modalities with Modalities with good resolutiongood resolution and and

image qualityimage quality, such as CT and MRI., such as CT and MRI. High-level structuresHigh-level structures such as the such as the

overall prostate or large regions (at overall prostate or large regions (at least 64least 64×64 pixels).×64 pixels).

However, …However, …

24

Related ResearchesRelated Researches- Summary -- Summary -

However, they are not suitable for However, they are not suitable for extracting features of extracting features of low-level low-level structuresstructures in in ultrasoundultrasound images. images.

Any size of the window or wavelet Any size of the window or wavelet basis:basis: Too large for Too large for region boundary integrityregion boundary integrity.. Too small for Too small for reliable statisticsreliable statistics..

Outline

25

Problem FormulationProblem Formulation- Resources -- Resources -

Average image size 188.6Average image size 188.6×346.3 ×346.3 pixels.pixels.

Average cancerous region size Average cancerous region size 2920.3 pixels; that is smaller than a 2920.3 pixels; that is smaller than a circle with radius of 30.5 pixels!circle with radius of 30.5 pixels!

Original TRUS Image Prostate Outline

Cancerous Region OutlineTZ Outline

26

Problem FormulationProblem Formulation- Objectives -- Objectives -

To come up with a new set of features that To come up with a new set of features that can help differentiate cancerous regions in a can help differentiate cancerous regions in a TRUS image from the rest of the prostate.TRUS image from the rest of the prostate.

Desirable criteria:Desirable criteria: The features can be applied to analyze The features can be applied to analyze low-level low-level

structuresstructures, such as the cancerous regions (~30-, such as the cancerous regions (~30-radius circle).radius circle).

The The boundary integrityboundary integrity of each region-of-interest of each region-of-interest should be well preserved.should be well preserved.

The features should be The features should be isotropicisotropic.. The features should be The features should be discriminativediscriminative enough to enough to

differentiate cancerous regions from the benign differentiate cancerous regions from the benign regions.regions.

Outline

27

Proposed Feature Proposed Feature Extraction MethodExtraction Method

- Overview -- Overview -

Feature Evaluation

PDF Estimation

FIS

Raw-Based Feature Extraction Model-Based Feature Extraction

Symmetry

RegionSegmentation

Image Registration

Greylevel Texture Spatial Location

MI Evaluation

Membership Functions

Fuzzy Rules

input

outputFeature Design

Parameters

Feature Selection

design only

Outline

Region Geometry

28


- Region Segmentation -- Region Segmentation - Some region segmentation methods Some region segmentation methods

that I have tried:that I have tried: Graph-theory-based methodGraph-theory-based method by by

constructing Minimum Spanning Tree constructing Minimum Spanning Tree (MST).(MST).

ThresholdingThresholding on histogram. on histogram.

Graph-theory-based method Thresholding-based method

29


- Region Segmentation -- Region Segmentation - Thresholding-based method:Thresholding-based method:

40 60 80 100 120 140 160 180 200 220 2400

200

400

600

800

1000

1200

1400

1600

1800

2000Histogram

Original Gaussian Blurred Histogram

GreylevelSegmentation

ZonalSegmentation

MorphologicalOperators:

“open” and “holes”

Resulting Segmentation

Overview

30


- Image Registration -- Image Registration - Prostates have different shapes on Prostates have different shapes on

TRUS images due to:TRUS images due to: Different physical shapes.Different physical shapes. Different probes (e.g. side-fire, end-Different probes (e.g. side-fire, end-

fire).fire). Prostates may not be located at the Prostates may not be located at the

centre of the image.centre of the image. Original Image

31


- Image Registration -- Image Registration - The idea is to deform all the prostates The idea is to deform all the prostates

into a common model shape:into a common model shape: The model shape should allow the The model shape should allow the ease ease

of specifying the relative spatial locationof specifying the relative spatial location of a given point with respect to the of a given point with respect to the whole prostate.whole prostate.

The model shape should be similar to an The model shape should be similar to an average prostate outlineaverage prostate outline..

The model shape should be The model shape should be reflectionally reflectionally symmetricsymmetric about the vertical axis located about the vertical axis located at the centre of the image.at the centre of the image.

32


- Image Registration -- Image Registration - A compromise:A compromise:

Model Binary Mask

33


- Image Registration -- Image Registration -Affine

Transformation

Outline-Based

Texture-Based

Model-Based

Fluid-Landmark-Based Transformation

Define Landmarks

Estimate Optimal Trajectories

Calculate Velocity Vectors

Interpolate Missing Pixels

Original Image

Step 2

Step 2

Step 3;

Model Binary Mask

34


- Image Registration -- Image Registration - Define Define landmarkslandmarks::

16 equally spaced landmarks on the 16 equally spaced landmarks on the prostate outline.prostate outline.

2 equally spaced landmarks on the 2 equally spaced landmarks on the vertical axis.vertical axis.

No medical knowledge of the No medical knowledge of the anatomical structure is required.anatomical structure is required.

Subject Landmarks Model Landmarks

35


- Image Registration -- Image Registration - Lagrangian trajectoryLagrangian trajectory::

The initial, intermediate, and final The initial, intermediate, and final positions.positions.

Velocity vectorsVelocity vectors:: Displacement of the position of a Displacement of the position of a

landmark (in a unit of time).landmark (in a unit of time).Subject LandmarksModel Landmarks

0

00 ,),(t

nnn dttxvxtx

subjectxx )1,(

)2,(x

)3,(x

)3,(xv

)2,(xv T=3

)1,(),(),( txtxtxv nnn

36


- Image Registration -- Image Registration - Estimate optimal trajectories:Estimate optimal trajectories:

Minimize:Minimize:

Iterative gradient decent:Iterative gradient decent:

energy quadraticerror distance

),()),(()),((minarg),(ˆ txPtxDtx nn

txn

n

),(

)),((

),(

)),((),(),(1 tx

txP

tx

txDtxtx

nk

nk

nk

nknknk

Tt

TtxTx

tx

txD modelnnk

nk

nk

,0

],),([2

),(

)),(( ,1

N

mtxv

mkmkmknk

N

mtxv

mkmkmknk

txv

mkmkmk

mknkN

m

T

txv

mkmknk

nk

m

m

mm

txtxtxtxK

txtxtxtxK

txtxtx

txtxKtxtx

tx

txP

1)(

1

1)1(

1

)1(1

)1(

,

,

,,

)]1,(),([)),(),,((2

)],()1,([))1,(),1,((2

)],()1,([),(

)),(),,(()],()1,([

),(

)),((

37


- Image Registration -- Image Registration - Interpolate the Interpolate the optimal velocity optimal velocity

vectorsvectors for the whole image space: for the whole image space: Optimal velocity vectors of the Optimal velocity vectors of the

landmarks:landmarks:

Optimal velocity vectors of the whole Optimal velocity vectors of the whole image space:image space:

)1,(ˆ),(ˆ),(ˆ txtxtxv nnn

),(ˆ)),(ˆ),,(ˆ()),,(ˆ(),(ˆ1 1

1 txvtxtxKxtxKtxv mmn

N

n

N

mn

38


- Image Registration -- Image Registration - Optimal velocity vectors:Optimal velocity vectors:

Interpolate the Interpolate the optimal Lagrangian optimal Lagrangian trajectoriestrajectories for the whole image: for the whole image:

50 100 150 200 250 300 350

50

100

150

200

Velocity Vectors for t=3

50 100 150 200 250 300 350

50

100

150

200

Velocity Vectors for t=2

)2,(ˆ txv )3,(ˆ txv

T

t

txvxTx2

),(ˆ),(

39


- Image Registration -- Image Registration - Interpolating missing pixels in the Interpolating missing pixels in the

resulting image using linear resulting image using linear interpolation.interpolation.

Marked Subject ImageMarked Deformed Image

Before deformation After deformation

40


- Image Registration -- Image Registration - Now, we can easily measure spatial Now, we can easily measure spatial

location and symmetry!location and symmetry! Original images:Original images:

Registered Images:Registered Images:

Original Image

prostate14a088 prostate6a088 prostate8ba088 prostate5a088

Overview

41


- Greylevel -- Greylevel - Blur with Gaussian filter.Blur with Gaussian filter. Design parameter:Design parameter: Take average over each region-of-Take average over each region-of-

interest.interest.

TRUS Pixel-BasedGreylevel (GL)

Region-BasedGreylevel (GL)

Overview

42


- Texture -- Texture - GLDM with different window size.GLDM with different window size. Design parameter:Design parameter:ROIw

Pix

el-

Base

dR

eg

ion

-Base

d

CON MEA ENT IDM ASM

Overview

Equations

43


- Symmetry -- Symmetry - Difference from flipped feature Difference from flipped feature

images.images. Design parameter: none.Design parameter: none.

Greylevel-Symmetry(GS)

Pixel-Based

Overview

Texture-Symmetry(GS)

Region-BasedPixel-Based beforeinverse-deformation

44

Proposed Feature Proposed Feature Extraction MethodExtraction Method- Spatial Location -- Spatial Location -

Define coordinate system using a Define coordinate system using a “cone”.“cone”.

Design parameter:Design parameter:centrey

),( r

45

Proposed Feature Proposed Feature Extraction MethodExtraction Method- Spatial Location -- Spatial Location -

Spatial Radius (SR): Spatial Radius (SR): 0 at origin, 1 at the 0 at origin, 1 at the perimeter.perimeter.

Spatial Angle (SA):Spatial Angle (SA): 0 at top, 1 at bottom. 0 at top, 1 at bottom.Spatial-Radius(SR)

Pixel-Based

Spatial-Angle(SA)

Region-BasedPixel-Based beforeinverse-deformation

Overview

46


- Region Geometry -- Region Geometry - Region Area (RA)Region Area (RA) = number of = number of

pixels.pixels. Region Roundness (RR)Region Roundness (RR) = =

““perimeter of a circle with the same perimeter of a circle with the same area” divided byarea” divided by

““perimeter of the region”.perimeter of the region”.

Region Area (RA) Region Roundness (RR)

meterRegionPeri

RegionArea /2

Overview

47


- Feature Evaluation -- Feature Evaluation - How to fine-tune design parameters?How to fine-tune design parameters? How to evaluate each feature?How to evaluate each feature? How to compare the features?How to compare the features?

Original TRUS ExpectedCancerous Region

SR ASM

48

Proposed Feature Proposed Feature Extraction MethodExtraction Method- PDF Estimation -- PDF Estimation -

We can analyze its We can analyze its probability probability density function (PDF)density function (PDF).. Parzen EstimationParzen Estimation is used. is used.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1

2

3

4

5

6

7

Pixel-Based PDFs of Greylevel feature.

P(x|Cancerous)

P(x|Benign)

P(x)

49

Proposed Feature Proposed Feature Extraction MethodExtraction Method- MI Evaluation -- MI Evaluation -

EntropyEntropy:: Measures the degree of uncertainty.Measures the degree of uncertainty.

Mutual informationMutual information between feature between feature and class:and class:

Measures the decrease in entropy with Measures the decrease in entropy with an introduction of a feature F.an introduction of a feature F.

Measures the interdependence between Measures the interdependence between class and feature.class and feature.

Bounds:Bounds:

Cc

cpcpCH )(log)()(

)|()();( FCHCHCFMI

)()|(0 CHFCH )();(0 CHCFMI

50

Proposed Feature Proposed Feature Extraction MethodExtraction Method- Feature Design - Feature Design

Parameters -Parameters - Using Using MI(F;C),MI(F;C), the optimal the optimal design design parameterparameter for each feature can be for each feature can be selected more objectively.selected more objectively. DDeessiiggnn PPaarraammeetteerrss CChhoosseenn DDeessiiggnn PPaarraammeetteerr

GL }4,2,1,75.0,5.0,25.0{ 1 CON }19,15,11,7,3{ROIw 9ROIw MEA }19,15,11,7,3{ROIw 9ROIw ENT }19,15,11,7,3{ROIw 7ROIw IDM }19,15,11,7,3{ROIw 7ROIw ASM }19,15,11,7,3{ROIw 7ROIw SR,SA }75,50,30,25,20,15,10,0{centrey 0centrey GS,TS n/ a n/ a RA,RR n/ a n/ a

51


- Feature Selection -- Feature Selection - Select only a Select only a subsetsubset of the features. of the features.

For For efficiencyefficiency, and sometimes , and sometimes accuracyaccuracy.. Need to eliminate:Need to eliminate:

uninformativeuninformative features … features … low MI(F;C)low MI(F;C).. redundantredundant features … features … high MI(Fhigh MI(F11:F:F22))..

52


- Feature Selection -- Feature Selection - Use Use MI(F;C)MI(F;C) to eliminate to eliminate

uninformativeuninformative features. features. PPiixxeell--BBaasseedd RReeggiioonn--BBaasseedd MMII((FF;;CC)) HH((FF)) %% MMII((FF;;CC)) HH((FF)) %%

GGLL 0.0488 0.5402 9.0% 0.0524 0.5402 9.7% CCOONN 0.043 0.5402 8.0% 0.1079 0.5402 20.0% MMEEAA 0.0575 0.5402 10.6% 0.1091 0.5402 20.2% EENNTT 0.0922 0.5402 17.1% 0.1118 0.5402 20.7% IIDDMM 0.07 0.5402 13.0% 0.0757 0.5402 14.0% AASSMM 0.0745 0.5402 13.8% 0.0824 0.5402 15.3% SSRR 0.059 0.5028 11.7% 0.1184 0.5028 23.5% SSAA 0.0228 0.5028 4.5% 0.0688 0.5028 13.7% GGSS 0.0045 0.5028 0.9% 0.0342 0.5028 6.8% TTSS 0.035 0.5028 7.0% 0.0627 0.5028 12.5% RRAA n/ a n/ a n/ a 0.112 0.5402 20.7% RRRR n/ a n/ a n/ a 0.0734 0.5402 13.6%

Back

53

%% GGLL CCOONN MMEEAA EENNTT IIDDMM AASSMM SSRR SSAA GGSS TTSS RRAA RRRR GGLL 100.0 58.3 56.4 55.7 57.8 55.1 49.9 51.7 48.5 45.7 64.0 59.7

CCOONN 58.3 100.0 74.4 58.7 58.5 54.4 56.5 60.4 48.2 36.6 64.2 67.4 MMEEAA 56.4 74.4 100.0 53.2 53.9 52.2 54.7 58.4 46.3 37.0 62.8 66.8 EENNTT 55.7 58.7 53.2 100.0 51.2 83.2 52.4 57.1 43.6 36.5 63.8 63.9 IIDDMM 57.8 58.5 53.9 51.2 100.0 44.4 48.1 52.9 41.3 35.6 62.1 58.5 AASSMM 55.1 54.4 52.2 83.2 44.4 100.0 46.9 54.5 40.6 33.6 62.9 59.4

SSRR 49.9 56.5 54.7 52.4 48.1 46.9 100.0 69.2 65.8 61.5 60.3 58.7 SSAA 51.7 60.4 58.4 57.1 52.9 54.5 69.2 100.0 69.8 65.3 63.5 61.5 GGSS 48.5 48.2 46.3 43.6 41.3 40.6 65.8 69.8 100.0 52.2 52.6 49.2 TTSS 45.7 36.6 37.0 36.5 35.6 33.6 61.5 65.3 52.2 100.0 42.7 37.5 RRAA 64.0 64.2 62.8 63.8 62.1 62.9 60.3 63.5 52.6 42.7 100.0 67.8 RRRR 59.7 67.4 66.8 63.9 58.5 59.4 58.7 61.5 49.2 37.5 67.8 100.0


- Feature Selection -- Feature Selection - Use Use MI(FMI(F11;F;F22)) to eliminate to eliminate redundantredundant

features.features.

FFeeaattuurree GGLL CCOONN MMEEAA EENNTT IIDDMM AASSMM SSRR SSAA GGSS TTSS RRAA RRRR MMII((FF;;CC)) 9.7% 20.0% 20.2% 20.7% 14.0% 15.3% 23.5% 13.7% 6.8% 12.5% 20.7% 13.6%

54


- Feature Selection -- Feature Selection - Checking the feature selection Checking the feature selection

visually:visually:

CON MEA ENT IDM ASMGL

GS TS SR SA RA RR

TRUS Expected

Overview

55


- Fuzzy Inferencing System - Fuzzy Inferencing System -- Each feature by itself is not Each feature by itself is not

discriminative enough.discriminative enough. Need to find out the Need to find out the relationshiprelationship

between the selected features by between the selected features by analyzing them analyzing them jointlyjointly (collectively). (collectively).

This thesis proposes to use aThis thesis proposes to use aFuzzy Inferencing System (FIS)Fuzzy Inferencing System (FIS).. The idea is to come up a set of The idea is to come up a set of fuzzy fuzzy

rulesrules that relate all the selected that relate all the selected features.features.

56


- Fuzzy Inferencing System - Fuzzy Inferencing System --

57


- Membership Functions -- Membership Functions - Design the Design the breakpointsbreakpoints of the of the

membership functionsmembership functions using using PDFsPDFs.. Inspect Inspect local minimalocal minima.. Inspect Inspect intersectionintersection..

Semi-automatic.Semi-automatic.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5

0.0700

0.4600

0.5400

0.5900

0.9600

0.08000.1200

0.21000.4200

0.52000.7300

0.79000.9200

0.3700

0.7600

P(x|Cancerous)P(x|Benign)P(x)

58


- Membership Functions -- Membership Functions - Chosen Chosen breakpointsbreakpoints and and fuzzinessfuzziness..

BBrreeaakkppooiinnttss FFuuzzzziinneessss GGLL [0, 0.778, 0.9225, 1] 0.05

CCOONN [0.03, 0.0462, 0.0659, 0.0722, 0.0829, 0.12] 0.001 MMEEAA [0, 0.1097, 0.1493, 0.1788, 0.1919, 0.2028, 0.2188, 0.35] 0.005 EENNTT [0.3, 0.4182, 0.4696, 0.5136, 0.5430, 0.5650, 0.6603, 0.8] 0.01 IIDDMM [0, 0.0448, 0.0704, 0.1153, 0.4546, 0.7] 0.01 AASSMM [0, 0.0422, 0.0548, 0.0928, 0.1392, 0.2741, 0.45] 0.005 SSRR [0, 0.4584, 0.5051, 0.5891, 0.7105, 0.7572, 0.8319, 1] 0.01 SSAA [0, 0.3700, 0.7600, 1] 0.05 GGSS [0, 0.0464, 0.0635, 0.4] 0.005 TTSS [0, 0.0373, 0.0625, 0.1042, 0.1319, 0.1736, 0.3] 0.001 RRAA [0, 0.0116, 0.0345, 0.0567, 0.0682, 0.08] 0.001 RRRR [0, 0.1207, 0.1637, 0.1837, 0.2267, 0.4] 0.005

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- Fuzzy Rules -- Fuzzy Rules - Generate fuzzy rules for each image:Generate fuzzy rules for each image:

)(1 xp

xMF1 MF2 MF3 MF4 MF5

)(2 xp

xMF1 MF2 MF3

Rule 4: if (FEATURE1 is MF1) and (FEATURE2 is MF2) then (BENIGN)

...

Rule 1: if (FEATURE1 is MF2) and (FEATURE2 is MF3) then (CANCEROUS)

60%

40%

Ratio3 = 0.6

Rule 2: if (FEATURE1 is MF3) and (FEATURE2 is MF3) then (CANCEROUS)

Rule 3: if (FEATURE1 is MF3) and (FEATURE2 is MF3) then (LIKELY-CANCEROUS)

Overview

60

AnalysisAnalysis

Some successful sample results:Some successful sample results:

OriginalTRUS

ExpectedCancerousRegion

ProposedFeatureImage

61

AnalysisAnalysis

Some less successful sample results:Some less successful sample results:

OriginalTRUS

ExpectedCancerousRegion

ProposedFeatureImage

62

PPrrooppoosseedd FFeeaattuurree SSeett 40.3% 53.48% 13.28% 32.13% 47.97% 20.81% 28.30% 10.24% 12.53% 2266..4488%%

AnalysisAnalysis

Comparison between proposed Comparison between proposed feature extraction method with other feature extraction method with other methods:methods:

FFeeaattuurree GGLL CCOONN MMEEAA EENNTT IIDDMM AASSMM SSRR SSAA GGSS TTSS RRAA RRRR MMII((FF;;CC)) 9.7% 20.0% 20.2% 20.7% 14.0% 15.3% 23.5% 13.7% 6.8% 12.5% 20.7% 13.6%

GGLL ++ GGLLDDMM IImmaaggee 11 36.75% IImmaaggee 22 53.72% IImmaaggee 33 5.76% IImmaaggee 44 11.32% IImmaaggee 55 34.57% IImmaaggee 66 16.28% IImmaaggee 77 18.44% IImmaaggee 88 12.22% IImmaaggee 99 1.38% OOvveerraallll 1166..9911%%

Individual region-based features:

Combined feature:Pixel- vs. Region-Based

57% improvement due to new features!!!

13% improvement due to FIS!

Outline

63

ConclusionsConclusions Large-Fluid-Landmark Deformation was used

to deform prostates into a common model shape.

PDFs were used to: Evaluate each feature individually using MI(F;C). Eliminate redundant features using MI(F1;F2). Design membership functions semi-automatically. Generate fuzzy rules automatically.

Fuzzy rules mimics radiologists’ medical knowledge.

13% improvement due to FIS! 57% improvement due to new features,

especially Spatial Location features.

Outline

64

Future WorksFuture Works

Investigate on Investigate on region segmentationregion segmentation that can best serve the proposed that can best serve the proposed feature extraction method.feature extraction method.

Fully Fully automateautomate the the membership membership function designfunction design using PDFs. using PDFs.

Define optimal Define optimal thresholdsthresholds for for classifyingclassifying the new feature. the new feature.

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Questions and Questions and Comments?Comments?

……

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ReferencesReferences Medical Basics:Medical Basics:

M. D. Rifkin, “Ultrasound of the Prostate: Imaging in the Diagnosis and Therapy M. D. Rifkin, “Ultrasound of the Prostate: Imaging in the Diagnosis and Therapy of Prostatic Disease”, 2nd Edition, Lippincott Williams and Wilkins, 1996.of Prostatic Disease”, 2nd Edition, Lippincott Williams and Wilkins, 1996.

Texture Analysis:Texture Analysis: A. H. Mir, M. Hanmandlu, S. N. Tandon, “Texture Analysis of CT Images”, IEEE A. H. Mir, M. Hanmandlu, S. N. Tandon, “Texture Analysis of CT Images”, IEEE

Engineering in Medicine and Biology, November / December 1995.Engineering in Medicine and Biology, November / December 1995. K. N. B. Prakash, A. G. Ramakrishnan, S. Suresh, T. W. P. Chow, “Fetal Lung K. N. B. Prakash, A. G. Ramakrishnan, S. Suresh, T. W. P. Chow, “Fetal Lung

Maturity Analysis Using Ultrasound Image Features”, IEEE Transactions on Maturity Analysis Using Ultrasound Image Features”, IEEE Transactions on Information Technology in Biomedicine, Vol. 6, No. 1, March 2002.Information Technology in Biomedicine, Vol. 6, No. 1, March 2002.

O. Basset, Z. Sun, J. L. Mestas,G. Gimenez, “Texture Analysis of Ultrasound O. Basset, Z. Sun, J. L. Mestas,G. Gimenez, “Texture Analysis of Ultrasound Images of the Prostate by Means of Co-occurrence Matrices”, Ultrasound Images of the Prostate by Means of Co-occurrence Matrices”, Ultrasound Imaging 15, 218-237 (1993).Imaging 15, 218-237 (1993).

Image Registration:Image Registration: Sarang C. Joshi and Michael I. Miller, “Landmark Matching via Large Sarang C. Joshi and Michael I. Miller, “Landmark Matching via Large

Deformation Diffeomorphisms,” IEEE Transactions on Image Processing, Vol. 9, Deformation Diffeomorphisms,” IEEE Transactions on Image Processing, Vol. 9, No. 8, August 2000.No. 8, August 2000.

Symmetry:Symmetry: Q. Li, S. Katsuragawa, K. Doi, “Improved contralateral subtraction images by use Q. Li, S. Katsuragawa, K. Doi, “Improved contralateral subtraction images by use

of elastic matching technique”, Medical Physics, 27 (8), August 2000.of elastic matching technique”, Medical Physics, 27 (8), August 2000. Feature Selection:Feature Selection:

R. Battiti, “Using Mutual Information for Selecting Features in Supervised R. Battiti, “Using Mutual Information for Selecting Features in Supervised Neural Net Learning”, IEEE Transactions on Neural Networks, Vol. 5, No. 4, July Neural Net Learning”, IEEE Transactions on Neural Networks, Vol. 5, No. 4, July 1994.1994.

Please see my thesis for all other references.Please see my thesis for all other references.

Documents

Eric K. T. Hui University of Waterloo, M.A.Sc. Seminar Wednesday, November 12, 2003