Quality Based Information Fusion in Fully Automatized Celiac Disease Diagnosis

Up to now, for most endoscopical computer aided celiac disease diagnosis approaches, image regions showing discriminative features have to be manually extracted by the physicians, prior to their automatized classification. This is obligatory to get idealistic and reliable data which is free from strong image degradations. On the one hand such a human interaction during endoscopy is subjective, expensive and tedious, but on the other hand state-of-the-art fully automatized selection corresponds to decreased classification accuracies compared to experienced human experts. In this work, a fully automatized approach is introduced which exploits the availability of a significant number of subimages within one original endoscopic image. A weighted decision-level and a weighted feature-level fusion method are introduced and investigated with respect to the achieved classification accuracies. The outcomes are compared with simple decision-level and feature-level fusion methods and the manual and the automatized patch selection. Finally, we show that the proposed feature-level fusion method outperforms all other automatized methods and comes close to manual patch selection.