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

1. 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

2. 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

3. 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

4. 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)

5. 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)

6. 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

7. 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

8. 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

9. 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

10. Experimental Framework • We focus only on the textural tissue patterns • Gastroscopic video 320240 pixels • Region of interest 128128 pixels

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

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

13. Results

14. Results

15. 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

16. 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

17. Thank you