Endoscopic Video Manifolds

摘要：

Postprocedural analysis of gastrointestinal (GI) endoscopic videos is a difficult task because the videos often suffer from a large number of poor-quality frames due to the motion or out-of-focus blur, specular highlights and artefacts caused by turbid fluid inside the GI tract. Clinically, each frame of the video is examined individually by the endoscopic expert due to the lack of a suitable visualisation technique. In this work, we introduce a low dimensional representation of endoscopic videos based on a manifold learning approach. The introduced endoscopic video manifolds (EVMs) enable the clustering of poor-quality frames and grouping of different segments of the GI endoscopic video in an unsupervised manner to facilitate subsequent visual assessment. In this paper, we present two novel inter-frame similarity measures for manifold learning to create structured manifolds from complex endoscopic videos. Our experiments demonstrate that the proposed method yields high precision and recall values for uninformative frame detection (90.91％ and 82.90％) and results in well-structured manifolds for scene clustering.

关键词： Endoscopy manifold learning video segmentation clustering

作者: Selen Atasoy Diana Mateus Joe Lallemand Alexander Meining Guang-Zhong Yang Nassir Navab

作者单位: Chair for Computer Aided Medical Procedures (CAMP), TU Munich, Germany Visual Information Processing Chair for Computer Aided Medical Procedures (CAMP), TU Munich, Germany Klinikum Rechts der Isar, TU Munich, Germany Visual Information Processing Group, Imperial College London, United Kingdom

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 437-445

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）