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Polyp-detection in Colonoscopy

Improve colon cancer detection by identifying polyps in colonoscopy videos using advanced features like GLCM and LBP. Achieve higher accuracy through robust data acquisition, feature extraction, and classifier training.

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Polyp-detection in Colonoscopy

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  1. Polyp-detection in Colonoscopy • Stefan Ameling • 2008 • stameling@uni-koblenz.de

  2. Medical Background: Colon • Length: ~ 1.5 m • Diameter: ~ 6 cm • „tubelike“

  3. Medical Background: Colon • Common diseases: • Colitis • Diverticulitis • Colon cancer

  4. Colon cancer • 70.000 cases/year (in Germany)‏ • one of the leading causes of cancer death worldwide: 655.000 deaths/year

  5. Colon polyps • Untreated polyps can develop into cancer. • Colonoscopy: cancer prevention (detect and remove polyps). • Problem: miss-rate (up to 25 %)‏

  6. System for computer-assisted detection • Supports the doctor during examination • Unsolved problem • Many approaches • Lack of good data

  7. General approach Acquire data (videos / images) including ground truth information Extract features Train classifier Test classifier } find „the best“ features

  8. 1. Data acquisition • Videos: Capture colonoscopy in hospital • „Ground truth“: time consuming

  9. 2. Feature extraction • Divide image into patches • Extract features: • Texture • Color • … • One feature-vector for each patch

  10. 3. Classifier training • Example: Support Vector Machine (SVM)‏ • We have: set of feature vectors, each belonging to one class (polyp or non-polyp)‏ • SVM: Hyperplane

  11. 4. Classifier testing • Test and training sets must be seperated! (e.g.: n-fold cross-validation)‏ • Possible results for the patches: • true positive (tp), false negative (fn)‏ • false positive (fp), true negative (tn)‏ • Receiver Operation Characteristics (ROC) Graph • Ordinate: Abscissa:

  12. Our approach • We have: 4 hours video of colonoscopy • Full HD (1920 x 1080)‏ • 4 scenes, each showing a different polyp • Varying distance, angle, illumination • Texture feature extraction: • Grey-level co-occurrence Matrix (GLCM)‏ • Local binary pattern

  13. Grey-level co-occurrence Matrix (GLCM)‏ • GLCM • Greyimage of size • Thus, is a matrix of size where is the number of possible grey-levels in • can be normalized by dividing each entry by the sum of all entries (→ probabilties)‏

  14. GLCM: example • The GLCM is parameterized by and • Here: and Image GLCM (not normalized)‏ 0 1 0 1 1 0 2 2 2 2 0 0 0 0 1 0 0 2

  15. GLCM: statistical features • e.g. homogeneity: • Energy, correlation, inertia, … • These statistical features can form a feature-vector.

  16. Local Binary Pattern (LBP)‏ • LBP-value (computed from each neighbourhood)‏ • All LBP-values form a histogram that can be used as a feature-vector Example neighbourhood 1 + 8 + 16 + 64 LBP = 89 Weights LBP 1 2 4 21 4 3 1 0 0 8 16 12 9 18 1 1 128 32 64 7 36 2 0 1 0

  17. Extension: Opponent-colour LBP • One LBP-histogram from each color-channel • Additionally: Intra-channel histograms: • center-pixel and neighbourhood from different color-channels • In total: 9 histograms form the feature-vector • → many dimensions

  18. Experiments • Data: 4 scenes • Feature-extraction: 4 different featuresets • GLCM 6 • GLCM 16 • LBP • OC-LBP • 4 different patch-sizes • Classifier-training and testing (LibSVM)‏ • Stratified 4-fold cross-validation

  19. ROC-graph (example)‏

  20. Results (AUC)‏ Scene Patchsize GLCM6 GLCM16 LBP OC-LPB 1 70 0.83 0.95 0.89 0.94 2 70 0.70 0.75 0.76 0.87 3 70 0.89 0.88 0.95 0.96 4 70 0.65 0.68 0.80 0.91 1 50 0.80 0.89 0.86 0.89 2 50 0.71 0.75 0.71 0.84 3 50 0.80 0.83 0.88 0.95 4 50 0.63 0.65 0.77 0.89 1 35 0.77 0.92 0.84 - 2 35 0.65 0.71 0.66 - 3 35 0.74 0.76 0.82 - 4 35 0.63 0.65 0.74 -

  21. Results • OC-LPB almost always the best • GLCM6 almost always the worst • GLCM performs worse on scene 4 • LPB performs worse on scene 2 • Independent from the features: • Scene 1 and 3: good results • Scene 2 and 4: worse results • No relation between feature and „polyp- types“

  22. Future Work • More video/image data • Method for ground truth aqcuisition • Test / develop more features • Realtime

  23. References • General • AMELING, S.: Polypen- und Tumordetektion in Koloskopie-Videos, Studienarbeit im Studiengang Computervisualistik, Universität Koblenz-Landau, 2008 • Miss-rates • BRESSLER, Brian ; PASZAT, Lawrence F. ; VINDEN, Christopher ; LI, Cindy; HE, Jingsong ; RABENECK, Linda: Colonoscopic miss rates for right sided colon cancer: a population-based analysis. In: Gastroenterology 127 (2004), Nr. 2, S. 452–456 • THOMSON, Alan ; AHNEN, Dennis ; RIOPELLE, John: Intestinal polypoid adenomas. In: eMedicine, The Continually Updated Clinical Reference (2007)‏ • Polyp Detection Methods • IAKOVIDIS, D.K. ; MAROULIS, D.E. ; KARKANIS, S. A.: An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy. In: Computers in Biology and Medicine 36 (2006), Nr. 10, S. 1084–1103 • Local Binary Patterns • Mäenpää, T.: The local binary pattern approach to texture analysis–extensions and applications. (2003) Dissertation, University of Oulu. • Grey-level co-occurrence Matrix • HARALICK, R. M. ; DINSTEIN, I. ; SHANMUGAM, K.: Textural features for image classification. In: IEEE Trans. Systems, Man, and Cybernetics 3 (1973), Nr. 6, S. 610–621

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