Automatic feature generation in endoscopic images

Ulrich Klank, Nicolas Padoy, Hubertus Feußner, Nassir Navab

Abstract: Fiber optic endoscopy is essential for minimally invasive surgery, but endoscopic images are very challenging for computer vision algorithms, since they contain many effects like tissue deformations, specular reflections, smoke, variable illumination and field of view. We developed a method to extract features from endoscopic images usable for scene analysis and classification. These features could be used with data from other sensors for workflow analysis and recognition.