This paper focuses on an approach for real-time metal extraction of x-ray images taken from modern x-ray machines like C-arms. Such machines are used for vessel diagnostics, surgical interventions, as well as cardiology, neurology and orthopedic examinations. They are very fast in taking images from different angles. For this reason, manual adjustment of contrast is infeasible and automatic adjustment algorithms have been applied to try to select the optimal radiation dose for contrast adjustment. Problems occur when metallic objects, e.g., a prosthesis or a screw, are in the absorption area of interest. In this case, the automatic adjustment mostly fails because the dark, metallic objects lead the algorithm to overdose the x-ray tube. This outshining effect results in overexposed images and bad contrast. To overcome this limitation, metallic objects have to be detected and extracted from images that are taken as input for the adjustment algorithm. In this paper, we present a real-time solution for extracting metallic objects of x-ray images. We will explore the characteristic features of metallic objects in x-ray images and their distinction from bone fragments which form the basis to find a successful way for object segmentation and classification. Subsequently, we will present our edge based real-time approach for successful and fast automatic segmentation and classification of metallic objects. Finally, experimental results on the effectiveness and performance of our approach based on a vast amount of input image data sets will be presented.
We present an adaptive distributed multimedia server architecture (ADMS) that builds upon the idea of offensive adaptivity, where the server proactively controls its layout through replication or migration of server components to recommended hosts. Proactive actions are taken when network or server resources become critical when fulfilling client demands. Recommendations are provided by a so-called "host recommender" which represents an integral part of Vagabond2 -- the middleware used for component distribution. Recommendations are based on measured or estimated server and network resource availabilities. Network distance and host resource metrics -- obtained from network and host resource services respectively -- may be communicated as MPEG-21 DIA descriptors. Finally we evaluate our architecture in a real-world streaming scenario.