Automatic reduction of wireless capsule endoscopy reviewing time based on factorization analysis. (May 2020)

• Record Type: Journal Article
• Authors: Biniaz, Abbas; Zoroofi, Reza Aghaeizadeh; Sohrabi, Masoud Reza
• Is Part Of: Biomedical signal processing and control. Volume 59(2020)
• Publication Date: 2020-05
• Subjects: Singular value decomposition -- GI tract video summarization -- Informative WCE frames -- Sliding window; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101897
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Efficient video summarization framework for WCE video. Applying singular value decomposition for keyframe selection. Extract keyframes using the hand-crafted and deep features. Reduce the poor quality frames in the WCE video. 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.