Reduction of capsule endoscopy reading times by unsupervised image mining

Iakovidis, Dimitris K.; Tsevas, Spyros; Polydorou, Andreas

doi:10.1016/j.compmedimag.2009.11.005

Cited by 90 publications

(58 citation statements)

References 28 publications

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“…In comparison, the endoscope video of our problem is more subjective and difficult for summarization. To our best knowledge, the state-of-the-art works for endoscope video summarization merely adopt wireless capsule endoscopy (WCE) data, such as [42][43][44], while ours focuses on the summarization of the traditional gastroscopic video. Although a gastroscopic video is obtained in a supervised condition and lasts approximately 20 min, it is also necessary to select some key frames to abstract the gastroscopic video.…”

Section: Related Workmentioning

confidence: 99%

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

Wang

Cong

Cao

et al. 2016

Artificial Intelligence in Medicine

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Section: Related Workmentioning

confidence: 99%

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

Wang

Cong

Cao

et al. 2016

Artificial Intelligence in Medicine

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“…Thus, the main difference between this method and other methods [15] is that the reading time details are inspected by two separate components, and this helps one to better understand not only the time for viewing a sequence but also the time used for seeking abnormal regions. Matching abnormal regions captured.…”

Section: Quality Attributes and Metrics In Kaskadamentioning

confidence: 99%

Evaluation of Multimedia Applications in a Cluster-Oriented Environment

Czarnul¹,

Dziubich²,

Krawczyk³

2012

Metrology and Measurement Systems

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In the age of Information and Communication Technology (ICT), Web and the Internet have changed significantly the way applications are developed, deployed and used. One of recent trends is modern design of web-applications based on SOA. This process is based on the composition of existing web services into a single scenario from the point of view of a particular user or client. This allows IT companies to shorten the producttime to market process. On the other hand, it raises questions about the quality of the application, trade-offs between quality factors and attributes and measurements of these. Services are usually hosted and executed in an environment managed by its provider that assures the quality attributes such as availability or throughput. Therefore, in this paper an attempt has been made to perform quality measurements towards the creation of efficient, dependable and user-oriented Web applications. First, the process of designing service-based applications is described. Next, metrics for subsequent measurements of efficiency, dependability and usability of distributed applications are presented. These metrics will assess the efforts and trade-offs in a Web-based application development. As examples, we describe a pair of multimedia applications which we have developed in our department and executed in a cluster-based environment. One of them runs in the BeesyCluster middleware and the second one in the Kaskada platform. For these applications we present results of measurements and conclude about relations between quality attributes in the presented application development model. This knowledge can be used to reason about such relations for new similar applications and be used in rapid and quality development of the latter.

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“…These methods focus on defining specific features such as colour or texture and then detecting the frames containing them in order to present the expert only the detected informative frames instead of the whole content of the endoscopic video. Recently, representative frame extraction for content summary has also been investigated to aid the postprocedural analysis of wireless capsule endoscopy [6]. The aim of our work is to cluster the GI endoscopic videos in an unsupervised manner in order to allow the expert to easily eliminate or visualise only the parts of interest during postprocedural analysis.…”

Section: Introductionmentioning

confidence: 99%

Endoscopic Video Manifolds

Atasoy

Mateus

Lallemand

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. 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.

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Reduction of capsule endoscopy reading times by unsupervised image mining

Cited by 90 publications

References 28 publications

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

Evaluation of Multimedia Applications in a Cluster-Oriented Environment

Endoscopic Video Manifolds

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