On the implicit handling of varying distances and gastrointestinal regions in endoscopic video sequences with indication for celiac disease

We have shown in previous work that problems inherent in the automated diagnosis of standard gastroscopic videos, such as distortion and noise handling, can be handled implicitly, to some extent, by using a one-class support vector machine (SVM) classifier. A video sequence of a standard endoscopic procedure is characterized by rapid changes of perspective towards an inspected area causing various shots at different distances as well as non-predictable transits through gastrointestinal regions. In this work we examine to what extent a one-class support vector machine combined with features based on local binary patterns (LBP) variants can be used to implicitly handle varying camera distances to the mucosa as well as the non-predictable topographical changes during endoscopy.