Automatic reduction of wireless capsule endoscopy reviewing time based on factorization analysis

Abstract

Wireless capsule endoscopy (WCE) is a painless and easy process to screening of the gastrointestinal (GI) tract. During WCE procedure, a huge amount of the endoscopy video frames is generated, however, a limited amount of data is actually useful for diagnosis. Manually reviewing all endoscopy frames is tedious, time-consuming and prone to physician error. In this paper, we propose a novel capsule video summarization framework to reduce WCE reviewing time using the factorization analysis based on sliding window singular value decomposition (SVD). Through the proposed approach, in a quality assessment stage, poor quality frames are removed from the endoscopy video. Adaptive sliding window SVD is then employed to extract the salient video frames. The average recall and precision were estimated by 0.92 and 0.94 for our local database. The quantitative and qualitative results demonstrate that the proposed approach outperforms the existing WCE keyframe extraction methods and provides video keyframes to the gastroenterologists in the clinical applications without discarding significant diagnosis information.