Exploiting Interframe Redundancies in the Lossless Compression of 3D Medical Images

Recent advances in digital technology have caused a huge increase in the use of 3D medical image data. In order to cope with large storage and transmission requirements, data compression is necessary. Although lossy techniques achieve higher compression ratios than lossless techniques, the latter are sometimes required, e.g., in medical environments. There exist a lot of lossless image compressors, but most of them do not exploit interframe correlations. In this paper, an evaluation is made of different approaches in removing interframe redundancies. It is shown that linear predictive techniques are not able to provide any compression improvement. Non-linear techniques, such as context-modeling do yield better results. This is mainly due to the special nature of 3D medical image data (i.e., large interslice distances, a lot of noise, etc.). In this paper, a new technique is proposed based on the 2D lossless image compressor JPEG-LS (which will be part of the new JPEG-2000 lossless image compression standard). A context-modeling scheme is developed which catches the interframe redundancies, and is integrated into JPEG-LS’ statistical modeling chain. The results show that typically 0.5 to 1 bit per pixel (10-20%) is gained. Keywords— interframe modeling, lossless medical image compression