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A Comparative Study of Texture Features for the Discrimination of Gastric Polyps in Endoscopic Video. D. Iakovidis 1 , D. Maroulis 1 , S.A. Karkanis 2 , A. Brokos 1. 1 University of Athens Department of Informatics & Telecommunications Realtime Systems & Image Processing Laboratory.

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slide1

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

in Endoscopic Video

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

1 University of Athens

Department of Informatics & Telecommunications

Realtime Systems & Image Processing Laboratory

2 Technological Educational of Lamia

Department of Informatics & Computer Technology

slide2

Gastric 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
slide3

Aim

Medicine

Computer Science

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
slide4

Previous Works

  • Detection of gastric ulser using edge detection
  • (Kodama et al. 1988)
  • Diagnosis of gastric carcinoma using epidemiological
  • data analysis
  • (Guvenir et al. 2004)
slide5

Previous 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)
slide6

Texture Spectrum Histogram

(Wang & He, 1990)

  • Greylevel images
  • 33 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
slide7

Local Binary Pattern Histogram

(Ojala, 1998)

  • Greylevel images
  • Inspired by the Texture Spectrum method
  • 33 neighborhood thresholded in 2 levels
  • Totally 28 = 256 possible TUs
  • Feature vectors formed by the NTU distribution
slide8

Texture Spectrum and Color Histogram Statistics

(Tjoa & Krishnan, 2003)

  • Color images (HSI)
  • Inspired by the Texture Spectrum 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 Cfrom each color channel C
slide9

Color 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
slide10

Experimental Framework

  • We focus only on the textural tissue patterns
  • Gastroscopic video 320240 pixels
  • Region of interest 128128 pixels
slide11

Experimental Framework

  • 1,000 Representative video frames
  • Verified polyp and normal samples
  • 4,000 non-overlapping sub-images 3232 pixels
slide12

Experimental Framework

  • Support Vector Machines (SVM)
  • 10-fold cross validation
  • Receiver Operating Characteristics (ROC)
  • Accuracy assessed using
  • the Area Under Characteristic (AUC)
slide15

Conclusions

  • We have considered texture as a primary
  • discriminative feature of gastric polyps
  • Four texture feature extraction methods were
  • considered
  • Their performance was compared using SVMs
  • and ROC analysis
slide16

Conclusions

  • The development of a CBMS for gastric polyp
  • detection is feasible
  • Color information enhances gastric polyp
  • discrimination
  • The discrimination performance of the spatial and
  • the wavelet domain color texture features is
  • comparable
  • The CBMSs developed for colon polyp detection
  • can reliably be used for gastric polyp detection