Wavelet-based medical image compression

In view of the increasingly important role played by digital medical imagin g in modern health care and the consequent blow up in the amount of image d ata that have to be economically stored and/or transmitted, the need for th e development of image compression systems that combine high compression pe rformance and preservation of critical information is ever growing. A power ful compression scheme that is based on the state-of-the-art in wavelet-bas ed compression is presented in this paper. Compression is achieved via effi cient encoding of wavelet zerotrees (with the embedded zerotree wavelet (EZ W) algorithm) and subsequent entropy coding. The performance of the basic v ersion of EZW is improved upon by a simple, yet effective, way of a more ac curate estimation of the centroids of the quantization intervals, at a negl igible cost in side information. Regarding the entropy coding stage, a nove l RLE-based coder is proposed that proves to be much simpler and faster yet only slightly worse than context-dependent adaptive arithmetic coding. A u seful and flexible compromise between the need for high compression and the requirement for preservation of selected regions of interest is provided t hrough two intelligent, yet simple, ways of achieving the so-called selecti ve 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 resul ts are presented that verify the superiority of our scheme over conventiona l block transform coding techniques (JPEG) with respect to both objective a nd subjective criteria. The high potential of our scheme for progressive tr ansmission, where the regions of interest are given the highest priority, i s also demonstrated. (C) 1999 Elsevier Science B.V. All rights reserved.