Real-time echocardiogram transmission protocol based on regions and visualization modes

Real-Time Adaptation to Time-Varying Constraints for Medical Video Communications

The wider adoption of mobile Health video communication systems in standard clinical practice requires real-time control to provide for adequate levels of clinical video quality to support reliable diagnosis. The latter can only be achieved with real-time adaptation to time-varying wireless networks' state to guarantee clinically acceptable performance throughout the streaming session, while conforming to device capabilities for supporting real-time encoding. We propose an adaptive video encoding framework based on multi-objective optimization that jointly maximizes the encoded video's quality and encoding rate (in frames per second) while minimizing bitrate demands. For this purpose, we construct a dense encoding space and use linear regression to estimate forward prediction models for quality, bitrate, and computational complexity. The prediction models are then used in an adaptive control framework that can fine-tune video encoding based on real-time constraints. We validate the system using a leave-one-out algorithm applied to ten ultrasound videos of the common carotid artery. The prediction models can estimate structural similarity quality with a median accuracy error of less than 1%, bitrate demands with deviation error of 10% or less, and encoding frame rate within a 6% margin. Real-time adaptation at a group of pictures level is demonstrated using the high efficiency video coding standard. The effectiveness of the proposed framework compared to static, nonadaptive approaches is demonstrated for different modes of operation, achieving significant quality gains, bitrate demands reductions, and performance improvements, in real-life scenarios imposing time-varying constraints. Our approach is generic and should be applicable to other medical video modalities with different applications.

An effective ultrasound video communication system using despeckle filtering and HEVC

The recent emergence of the high-efficiency video coding (HEVC) standard promises to deliver significant bitrate savings over current and prior video compression standards, while also supporting higher resolutions that can meet the clinical acquisition spatiotemporal settings. The effective application of HEVC to medical ultrasound necessitates a careful evaluation of strict clinical criteria that guarantee that clinical quality will not be sacrificed in the compression process. Furthermore, the potential use of despeckle filtering prior to compression provides for the possibility of significant additional bitrate savings that have not been previously considered. This paper provides a thorough comparison of the use of MPEG-2, H.263, MPEG-4, H.264/AVC, and HEVC for compressing atherosclerotic plaque ultrasound videos. For the comparisons, we use both subjective and objective criteria based on plaque structure and motion. For comparable clinical video quality, experimental evaluation on ten videos demonstrates that HEVC reduces bitrate requirements by as much as 33.2% compared to H.264/AVC and up to 71% compared to MPEG-2. The use of despeckle filtering prior to compression is also investigated as a method that can reduce bitrate requirements through the removal of higher frequency components without sacrificing clinical quality. Based on the use of three despeckle filtering methods with both H.264/AVC and HEVC, we find that prior filtering can yield additional significant bitrate savings. The best performing despeckle filter (DsFlsmv) achieves bitrate savings of 43.6% and 39.2% compared to standard nonfiltered HEVC and H.264/AVC encoding, respectively.