The impact of applying barrel distortion cor rection to endoscopic imagery in the context of automated celiac disease diagnosis is experimentally investigated. For a large set of feature extraction techniques, it is found that contrasting to intuition, no improvement but even significant result degradation of classification accuracy can be observed.For techniques relying on geometrical properties of the image material ("shape"), moderate improvements of classification accuracy can be achieved. Reasons for this somewhat unex pected results are discussed and ways how to exploit potential distortion correction benefits are sketched.
Distortion correction in two variants is applied to endoscopic duodenal imagery aiming at an improvement of automated classification of celiac disease affected mucosa patches. In a set of heterogeneous feature extraction techniques, only geometry and shape related ones are able to benefit from distortion correction, while for others, even a decrease of classification accuracy is observed. Different types of distortion correction do not lead to significantly different behaviour in the observed application scenario.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.