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.