Comparative assessment of feature extraction methods for visual odometry in wireless capsule endoscopy

Abstract

Wireless capsule endoscopy (WCE) enables the non-invasive examination of the gastrointestinal (GI) tract by a swallowable device equipped with a miniature camera. Accurate localization of the capsule in the GI tract enables accurate localization of abnormalities for medical interventions such as biopsy and polyp resection; therefore, the optimization of the localization outcome is important. Current approaches to endoscopic capsule localization are mainly based on external sensors and transit time estimations. Recently, we demonstrated the feasibility of capsule localization based—entirely—on visual features, without the use of external sensors. This technique relies on a motion estimation algorithm that enables measurements of the distance and the rotation of the capsule from the acquired video frames. Towards the determination of an optimal visual feature extraction technique for capsule motion estimation, an extensive comparative assessment of several state-of-the-art techniques, using a publicly available dataset, is presented. The results show that the minimization of the localization error is possible at the cost of computational efficiency. A localization error of approximately one order of magnitude higher than the minimal one can be considered as compromise for the use of current computationally efficient feature extraction techniques.