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Polyp-detection in Colonoscopy

Polyp-detection in Colonoscopy. Stefan Ameling 2008 stameling@uni-koblenz.de. Medical Background: Colon. Length: ~ 1.5 m Diameter: ~ 6 cm „tubelike“. Medical Background: Colon. Common diseases: Colitis Diverticulitis Colon cancer. Colon cancer. 70.000 cases/year (in Germany) ‏

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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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