Real time adaptive filtering for digital X-ray applications.

Over the last decade, many methods for adaptively filtering a data stream have been proposed. Those methods have applications in two dimensional imaging as well as in three dimensional image reconstruction. Although the primary objective of this filtering technique is to reduce the noise while avoiding to blur the edges, diagnostic, automated segmentation and surgery show a growing interest in enhancing the features contained in the image flow. Most of the methods proposed so far emerged from thorough studies of the physics of the considered modality and therefore show only a marginal capability to be extended across modalities. Moreover, adaptive filtering belongs to the family of processing intensive algorithms. Existing technology has often driven to simplifications and modality specific optimization to sustain the expected performances. In the specific case of real time digital X-ray as used surgery, the system has to sustain a throughput of 30 frames per second. In this study, we take a generalized approach for adaptive filtering based on multiple oriented filters. Mapping the filtering part to the embedded real time image processing while a user/application defined adaptive recombination of the filter outputs allow to change the smoothing and edge enhancement properties of the filter without changing the oriented filter parameters. We have implemented the filtering on a Cell Broadband Engine processor and the adaptive recombination on an off-the-shelf PC, connected via Gigabit Ethernet. This implementation is capable of filtering images of 5122 pixels at a throughput in excess of 40 frames per second while allowing to change the parameters in real time.