Abstract. Colonoscopy is one of the best methods for screening colon cancer. A variety of research groups have proposed methods for automatic detection of polyps in colonoscopic images to support the doctors during examination. However, the problem can still not be assumed as solved. The major drawback of many approaches is the amount and quality of images used for classifier training and evaluation. Our database consists of more than four hours of high resolution video from colonoscopies which were examined and labeled by medical experts. We applied four methods of texture feature extraction based on Grey-LevelCo-occurence and Local-Binary-Patterns. Using this data, we achieved classification results with an area under the ROC-curve of up to 0.96.
REF proposed methods for automatic detection of polyps in colonoscopy images to support surgeons during endoscopy examination.
Texture-based Polyp Detection in Colonoscopy
Bildverarbeitung für die Medizin
Computer Science