On the effects of de-interlacing on the classification accuracy of interlaced endoscopic videos with indication for celiac disease

Abstract

Interlaced scanning is a technique that has been widely in use to double the perceived frame rate without increasing the used bandwidth. Interlaced scanning is still in use by endoscopic video hardware today. Towards the development of an automated decision support system we focus on the evaluation of the impact of de-interlacing techniques on the accuracy of automated classification of endoscopic video data with indication for celiac disease. In a large experimental setup a variety of de-interlacing methods are evaluated using a set of feature extraction methods from the fields of pattern recognition and medical image analysis.