In recent years, digital images and videos of endoscopic screenings and surgical operations are recorded and archived by physicians with increasing frequency. The reason is that interventions can be documented in a much more expressive and informative way than with textual descriptions only. But an extensive, naive usage of endoscopic videos leads to huge amounts of video data that can easily exceed the storage capacity of hospital operators. Moreover, it is a very cumbersome task to retrieve specific information from such a large data set without efficient tools that are tailored to this very specific domain. Therefore, the purpose of endoViP is to investigate how this video data can be significantly reduced without losing any important information. This involves the development of efficient encoding strategies as well as the automatic identification of irrelevant scenes by means of content-based analysis methods. If irrelevant scenes are omitted and only the actually relevant scenes remain, also the browsing and retrieval performance can be improved considerably. In this context, novel mechanisms for time-efficient retrospective viewing and retrieval of specific relevant scenes will also be investigated. endoVip is an applied research project conducted at the Institute of Information Technology, Klagenfurt University and funded directly by […]
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Collaborative knowledge in big communities (known as Wisdom of the crowds) and self-organization of huge amount of networked participants have shown a lot of potential in diverse fields of computer science and information systems. Still, there are many scenarios, in particular in highly professional applications, where a critical mass of users (experts) cannot be found in order to safely create this knowledge. In several scenarios a top down model providing generally agreed domain knowledge can be used as common ground for communication. However, for other scenarios, diversity in terms of interest, motivation, information need, and many other characteristics prevent top down approaches, for which an à priori consensus between participating members must be found. Within the project at hand we focus on small and medium sized groups of people, who (i) have common interests but are too diverse for applying a commonly agreed model and (ii) who are bound to use multimedia systems to satisfy their needs. Further, we consider these people being experts with intense domain knowledge, such as medical doctors. In our project we plan to investigate (i) how communities of domain experts can be supported by multimedia information systems and (ii) how to leverage self-organization within those […]
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These days many surgeries are performed with a minimally-invasive approach, also known as medical endoscopy or keyhole-surgery. There are several special areas of medical endoscopy, the most frequent ones are arthroscopy (operations performed on joints), colonoscopy (procedures in the colon), and laparoscopy (operations in the abdomen). The endoscope is equipped with a light source, some fiber optics, and a high-resolution video camera, whose images are transmitted to a large display in the operation room. The images on this display are used by the operating endoscopists to control the endoscope and supervise actions performed with the operation instruments. Surgeons nowadays also record the real-time images of the endoscope as digital videos, stored as video segments for the most important parts of the surgery, or as a full video for the whole surgery, due to several reasons. In the EndoVIP2 project we focus on processing of these videos, performing video content analysis to detect relevant content, developing efficient storage methods for these videos, and providing efficient content search for that particular video data.
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