Wavelet-based medical image compression

Abstract

In view of the increasingly important role played by digital medical imaging in modern health care and the consequent blow up in the amount of image data that have to be economically stored and/or transmitted, the need for the development of image compression systems that combine high compression performance and preservation of critical information is ever growing. A powerful compression scheme that is based on the state-of-the-art in wavelet-based compression is presented in this paper. Compression is achieved via efficient encoding of wavelet zerotrees (with the embedded zerotree wavelet (EZW) algorithm) and subsequent entropy coding. The performance of the basic version of EZW is improved upon by a simple, yet effective, way of a more accurate estimation of the centroids of the quantization intervals, at a negligible cost in side information. Regarding the entropy coding stage, a novel RLE-based coder is proposed that proves to be much simpler and faster yet only slightly worse than context-dependent adaptive arithmetic coding. A useful and flexible compromise between the need for high compression and the requirement for preservation of selected regions of interest is provided through two intelligent, yet simple, ways of achieving the so-called selective compression. The use of the lifting scheme in achieving compression that is guaranteed to be lossless in the presence of numerical inaccuracies is being investigated with interesting preliminary results. Experimental results are presented that verify the superiority of our scheme over conventional block transform coding techniques (JPEG) with respect to both objective and subjective criteria. The high potential of our scheme for progressive transmission, where the regions of interest are given the highest priority, is also demonstrated.