1. A Comparative Study of Texture Features for the Discrimination of Gastric Polyps in Endoscopic Video 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 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

2. Gastric Cancer & Polyps Gastric Ca is the 2 nd 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 2 nd 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 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

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) 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) 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 Greylevel images 3  3 neighborhood thresholded in 3 levels V 0 central pixel, V i neighboring pixels, i =1, 2, …8 Texture Unit TU = {E 1, E 2,…, E 8 } Totally 3 8 = 6561 possible TUs Feature vectors formed by the N TU distribution Greylevel images 3  3 neighborhood thresholded in 3 levels V 0 central pixel, V i neighboring pixels, i =1, 2, …8 Texture Unit TU = {E 1, E 2,…, E 8 } Totally 3 8 = 6561 possible TUs Feature vectors formed by the N TU distribution (Wang & He, 1990)

7. Local Binary Pattern Histogram (Ojala, 1998) Greylevel images Inspired by the Texture Spectrum method 3  3 neighborhood thresholded in 2 levels Totally 2 8 = 256 possible TUs Feature vectors formed by the N TU distribution Greylevel images Inspired by the Texture Spectrum method 3  3 neighborhood thresholded in 2 levels Totally 2 8 = 256 possible TUs Feature vectors formed by the N TU distribution

8. Texture Spectrum and Color Histogram Statistics (Tjoa & Krishnan, 2003) Color images (HSI) Inspired by the Texture Spectrum method Feature vectors formed by 1 st order statistics on the N TU 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) Inspired by the Texture Spectrum method Feature vectors formed by 1 st order statistics on the N TU distribution in the I-channel: Energy & Entropy Mean, Standard deviation, Skew & Kurtosis In addition color features  C from each color channel C

9. Color Wavelet Covariance (Karkanis et al, 2003) Color images (I 1 I 2 I 3 ) 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 (I 1 I 2 I 3 ) 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 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 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) Support Vector Machines (SVM) 10-fold cross validation Receiver Operating Characteristics (ROC) Accuracy assessed using the Area Under Characteristic (AUC)

13. 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 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 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