Degradation adaptive texture classification: A case study in celiac disease diagnosis brings new insight

Abstract

Degradation adaptive texture classification has been claimed to be a powerful instrument for classifying images suffering from degradations of dissimilar extent. The main goal of this framework is to separate the image databases into smaller sets, each showing a high degree of similarity with reference to degradations. Up to now, only scenarios with different types of synthetic degradations have been investigated. In this work we generalize the adaptive classification framework and introduce new degradation measures to extensively analyze the effects of the approach on real world data for the first time. Especially computer aided celiac disease diagnosis based on endoscopic images, which has become a major field of research, is investigated. Due to the weak illuminations and the downsized sensors, the images often suffer from various distortions and the type as well as the strength of these degradations significantly varies over the image data. In a large experimental setup, we show that the average classification accuracies can be improved significantly.