A Comparative Study of Texture Features for the Discrimination of Gastric Polyps

A Comparative Study of Texture Features for the Discrimination

of Gastric Polypsin Endoscopic Video

A Comparative Study of Texture Features for the Discrimination

of Gastric Polypsin Endoscopic Video

D. Iakovidis1, D. Maroulis1, S.A. Karkanis2, A. Brokos1

D. Iakovidis1, D. Maroulis1, S.A. Karkanis2, A. Brokos1

1 University of Athens Department of Informatics & Telecommunications Realtime Systems & Image Processing Laboratory

1 University of Athens Department of Informatics & Telecommunications Realtime Systems & Image Processing Laboratory 2 Technological Educational of Lamia Department of Informatics & Computer Technology

2 Technological Educational of Lamia Department of Informatics & Computer Technology

Gastric Cancer & PolypsGastric Cancer & Polyps

• Gastric Ca is the 2nd Ca-related

cause of death

• Rarely alarming symptoms

• >40% appear as polyps

• Gastric polyps are visible tissue

masses protruding from the gastric mucosa

• Adenomatous polyps are usually

precancerous

• Gastroscopy is a screening

procedure with which polyp growth can be

prevented

• Gastric Ca is the 2nd Ca-related

cause of death

• Rarely alarming symptoms

• >40% appear as polyps

• Gastric polyps are visible tissue

masses protruding from the gastric mucosa

• Adenomatous polyps are usually

precancerous

• Gastroscopy is a screening

procedure with which polyp growth can be

prevented

AimAimMedicineMedicine

Computer ScienceComputer Science

Computer-Based Medical System (CBMS)

to support the detection of gastric polyps

Computer-Based Medical System (CBMS)

to support the detection of gastric polyps• Increase endoscopists ability for polyp

localization• Reduction of the duration of the endoscopic procedure• Minimization of experts’ subjectivity

• Increase endoscopists ability for polyp localization• Reduction of the duration of the endoscopic procedure• Minimization of experts’ subjectivity

Previous WorksPrevious Works

• Detection of gastric ulser using edge

detection(Kodama et al. 1988)

• Diagnosis of gastric carcinoma using

epidemiological data analysis (Guvenir et al. 2004)

• Detection of gastric ulser using edge

detection(Kodama et al. 1988)

• Diagnosis of gastric carcinoma using

epidemiological data analysis (Guvenir et al. 2004)

Previous WorksPrevious Works

• Detection of colon polyps using texture

analysis

1. Texture Spectrum Histogram (TS)(Karkanis et al, 1999) (Kodogiannis et al,

2004)

2. Texture Spectrum & Color Histogram

Statistics (TSCHS)(Tjoa & Krishnan, 2003)

3. Color Wavelet Covariance (CWC)(Karkanis et al, 2003)

4. Local Binary Patterns (LBP)(Zheng et al, 2004)

• Detection of colon polyps using texture

analysis

1. Texture Spectrum Histogram (TS)(Karkanis et al, 1999) (Kodogiannis et al,

2004)

2. Texture Spectrum & Color Histogram

Statistics (TSCHS)(Tjoa & Krishnan, 2003)

3. Color Wavelet Covariance (CWC)(Karkanis et al, 2003)

4. Local Binary Patterns (LBP)(Zheng et al, 2004)

Texture Spectrum HistogramTexture Spectrum Histogram• Greylevel images

• 33 neighborhood thresholded in 3

levels

• V0 central pixel, Vi neighboring

pixels, i =1, 2, …8

• Texture Unit TU = {E1, E2,…, E8}

• Totally 38 = 6561 possible TUs

• Feature vectors formed by the NTU

distribution

• Greylevel images


levels

• V0 central pixel, Vi neighboring

pixels, i =1, 2, …8

• Texture Unit TU = {E1, E2,…, E8}



distribution

0

0

0

2

1

0

VVif

VVif

VVif

E

i

i

i

i

0

0

0

2

1

0

VVif

VVif

VVif

E

i

i

i

i

8

1

13i

iiTU EN

8

1

13i

iiTU EN

(Wang & He, 1990)

Local Binary Pattern HistogramLocal Binary Pattern Histogram

(Ojala, 1998)


• Inspired by the Texture Spectrum

method


levels



distribution



method


levels



distribution

0

0'

1

0

VVif

VVifE

i

ii

0

0'

1

0

VVif

VVifE

i

ii

8

1

1' 2i

iiELBP

8

1

1' 2i

iiELBP

Texture Spectrum and Color Histogram StatisticsTexture Spectrum and Color Histogram Statistics(Tjoa & Krishnan, 2003)

• Color images (HSI)


method

• Feature vectors formed by 1st order

statistics on the NTU distribution in the I-channel:

• Energy & Entropy • Mean, Standard deviation, Skew & Kurtosis

• In addition color features C from each

color channel C

• Color images (HSI)


method

• Feature vectors formed by 1st order

statistics on the NTU distribution in the I-channel:

• Energy & Entropy • Mean, Standard deviation, Skew & Kurtosis

• In addition color features C from each

color channel C

1

0

)()(2

1

L

iC

L

LiCC iHistiHist

1

0

)()(2

1

L

iC

L

LiCC iHistiHist

Color Wavelet CovarianceColor Wavelet Covariance

(Karkanis et al, 2003)

• Color images (I1I2I3)

• Discrete Wavelet Frame Transform

(DWFT) on each channel C

• Co-occurrence statistics F on each

wavelet band B(k)

• Feature vectors formed by the

Covariance of the cooccurrence statistics between the color

channels

• Color images (I1I2I3)

• Discrete Wavelet Frame Transform

(DWFT) on each channel C

• Co-occurrence statistics F on each

wavelet band B(k)

• Feature vectors formed by the

Covariance of the cooccurrence statistics between the color

channels

)()()(, , kB

CkB

CkBCC

j

m

j

l

j

mlFFCovCWC )()()(

, , kBC

kBC

kBCC

j

m

j

l

j

mlFFCovCWC

Experimental FrameworkExperimental Framework

• We focus only on the textural

tissue patterns• Gastroscopic video 320240 pixels

• Region of interest 128128

pixels

• We focus only on the textural

tissue patterns• Gastroscopic video 320240 pixels

• Region of interest 128128

pixels


• 1,000 Representative video

frames

• Verified polyp and normal

samples

• 4,000 non-overlapping sub-

images 3232 pixels

• 1,000 Representative video

frames

• Verified polyp and normal

samples

• 4,000 non-overlapping sub-

images 3232 pixels


• Support Vector Machines

(SVM)

• 10-fold cross validation

• Receiver Operating

Characteristics (ROC)

• Accuracy assessed usingthe Area Under Characteristic

(AUC)

• Support Vector Machines

(SVM)

• 10-fold cross validation

• Receiver Operating

Characteristics (ROC)

• Accuracy assessed usingthe Area Under Characteristic

(AUC)

ResultsResults

Method Accuracy (AUC)

1 TS 75.2 2.6 %

2 LBP 80.6 2.5 %

3 TSCHS 87.5 2.1 %

4 CWC 88.6 2.3 %

Method Accuracy (AUC)

1 TS 75.2 2.6 %

2 LBP 80.6 2.5 %

3 TSCHS 87.5 2.1 %

4 CWC 88.6 2.3 %

ResultsResults

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1 - Specificity

Sens

itivi

ty

TSLBPTSCHSCWC

ConclusionsConclusions

• We have considered texture as a

primary discriminative feature of gastric

polyps

• Four texture feature extraction

methods wereconsidered

• Their performance was compared

using SVMs and ROC analysis

• We have considered texture as a

primary discriminative feature of gastric

polyps

• Four texture feature extraction

methods wereconsidered

• Their performance was compared

using SVMs and ROC analysis

ConclusionsConclusions

• The development of a CBMS for

gastric polyp detection is feasible

• Color information enhances

gastric polyp discrimination

• The discrimination performance of

the spatial andthe wavelet domain color texture

features is comparable

• The CBMSs developed for colon

polyp detectioncan reliably be used for gastric polyp

detection

• The development of a CBMS for

gastric polyp detection is feasible

• Color information enhances

gastric polyp discrimination

• The discrimination performance of

the spatial andthe wavelet domain color texture

features is comparable

• The CBMSs developed for colon

polyp detectioncan reliably be used for gastric polyp

detection

Thank youThank you

Documents

A Comparative Study of Texture Features for the Discrimination of Gastric Polyps