A novel tool for summarization of arthroscopic videos

Lux, Mathias; Marques, Oge; Schöffmann, Klaus; Böszörményi, László; Lajtai, G.

doi:10.1007/s11042-009-0353-1

Cited by 38 publications

(18 citation statements)

References 17 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Lux et al [132] present a static summarization algorithm for the domain of arthroscopy (an endoscopy type that is hardly ever addressed in the literature). It generates a single result image composed of a predefined number of most representative frames.…”

Section: Summarizationmentioning

confidence: 99%

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

Self Cite

109

View full text Add to dashboard Cite

In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with realtime assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.

show abstract

Section: Summarizationmentioning

confidence: 99%

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

Self Cite

109

View full text Add to dashboard Cite

show abstract

“…Lux et al [17] proposed to use global image features with a k-means clustering approach to create static image summaries of arthroscopic videos. More specifically, color and edge/texture features of all frames of a video are used to create a predefined number of clusters (e.g., 3 or 5).…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, these two requirements ensure that the selected keyframes show different situations of the video and hence give a good overview of the whole intervention. Finally, the last condition ensures that only frames with clearly visible content are selected, because endoscopic video typically contains a high amount of blurry frames [17,21]. Fig.…”

Section: Overviewmentioning

confidence: 99%

“…Videos of medical endoscopy have very specific characteristics that make content analysis a challenging task (see also in our previous work [17] that covers summarization of arthroscopic videos). First of all, the videos are stored as recorded during the intervention, without any editing.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the surgeon can enable zoom mode, to see a full frame without borders (right). summarization methods (e.g., the one we proposed earlier in [17]). They can also be used to improve the quality of printed documentation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Keyframe extraction in endoscopic video

Schoeffmann

Fabro

Szkaliczki

et al. 2014

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

In medical endoscopy more and more surgeons archive the recorded video streams in a long-term storage. One reason for this development, which is enforced by law in some countries, is to have evidence in case of lawsuits from patients. Another more practical reason is to allow later inspection of previous procedures and also to use parts of such videos for research and for training. However, due to the dramatic amount of video data recorded in a hospital on a daily basis, it is very important to have good preview images for these videos in order to allow for quick filtering of undesired content and for easier browsing through such a video archive. Unfortunately, common shot detection and keyframe extraction methods cannot be used for that video data, because these videos contain unedited and highly similar content, especially in terms of color and texture, and no shot boundaries at all. We propose a new keyframe extraction approach for this special video domain and show that our method is significantly better than a previously proposed approach.

show abstract