Polyp-detection in Colonoscopy Stefan Ameling 2008

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

Medical Background: Colon Common diseases: Colitis Diverticulitis Colon cancer

cases/year (in Germany)‏ one of the leading causes of cancer death worldwide: deaths/year

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

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

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

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

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

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

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:

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

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)‏

GLCM: example Image GLCM (not normalized)‏ 0 The GLCM is parameterized by and Here: and

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

Local Binary Pattern (LBP)‏ Weights Example neighbourhood LBP = 89 LBP LBP-value (computed from each neighbourhood)‏ All LBP-values form a histogram that can be used as a feature-vector

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

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

ROC-graph (example)‏

Results (AUC)‏ ScenePatchsizeGLCM6GLCM16LBP OC-LPB

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“

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

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