Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data

Lossless compression of dynamic 2-D+t and 3-D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block-and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block-and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53 dB and 0.28 dB for CT data, as well as 1.04 dB and 1.90 dB for MR data, respectively, while the bit rate is reduced at the same time.