Novel Prediction Based Technique for Efficient Compression of Medical Imaging Data

The annual volume of imaging data in modern paperless hospitals can approach up to 10 terabytes, heavily pressing the storage and transmission requirements. Utilizing efficient compression techniques for those data in order to reduce associated costs is very attractive from both viewpoints: financial and organizational. Although lossy techniques can yield better compression results, due to possible compression artifacts in the compressed image, they are less favored compared to lossless compression techniques in certain medical applications such as image-based diagnosis, archival etc. Moreover, new approaches in medical imaging such as 3D and 4D imaging and bio–modeling produce even greater amounts of image data. For efficient storage and transmission of those data and utilization of systems that exploit 3D and 4D imaging technologies, compression is inevitable. In this field, at least certain parts of images are required to be stored and transmitted without any loss of information. The lossless compression algorithm that we propose can also be efficiently employed for at least those vital parts of interest in this kind of applications. We propose a higly adaptive prediction-based lossless compression algorithm which models nontrivial image structures through selective blends of static predictors.