Unsupervised summarisation of capsule endoscopy video

D. K. Iakovidis, S. Tsevas, D. Maroulis, A. Polydorou
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引用次数: 27

Abstract

Capsule endoscopy is a non-invasive imaging technique commonly used for screening of the entire small intestine. It is performed by a wireless swallowable endoscopic capsule capable of transmitting thousands of video frames per examination. The visual inspection of the vast amount of images acquired during such an examination is a subjective and highly time consuming task even for experienced gastroenterologists. In this paper we propose a novel approach to the reduction of the number of the video frames to be inspected so as to enable faster inspection of the endoscopic video. It is based on symmetric non-negative matrix factorisation initialised by the fuzzy c-means algorithm and it is supported by non-negative Lagrangian relaxation to extract a subset of video frames containing the most representative scenes from a whole endoscopic examination. The experimental evaluation of the proposed approach was tested on annotated endoscopic videos with frames displaying ulcers, bleedings and normal tissues from various sites in the small intestine. The results demonstrate that the video summary produced consists of representative frames from all the abnormal findings and the normal tissues of the input video.
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胶囊内窥镜视频的无监督总结
胶囊内窥镜是一种非侵入性成像技术,通常用于整个小肠的筛查。它是由一个无线可吞内窥镜胶囊执行的,每次检查可以传输数千个视频帧。在这种检查中获得的大量图像的目视检查是一项主观且耗时的任务,即使对于经验丰富的胃肠病学家也是如此。在本文中,我们提出了一种新的方法来减少要检查的视频帧的数量,以便能够更快地检查内窥镜视频。它基于模糊c均值算法初始化的对称非负矩阵分解,并得到非负拉格朗日松弛的支持,从整个内镜检查中提取包含最具代表性场景的视频帧子集。该方法的实验评估在带注释的内窥镜视频上进行了测试,视频显示了小肠不同部位的溃疡、出血和正常组织。结果表明,所生成的视频摘要由输入视频中所有异常发现和正常组织的代表性帧组成。
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