In this paper, we investigate embedded
region of Interest (ROI) image coding of
mammograms. This coding algorithm is based on
combining Set Partitioning in Hierarchical Trees
(SPIHT) scheme and reversible wavelet transforms.
We will show that this combination produces both
progressive and perfect reconstruction features. The
benefit of such approach is that it offers the
possibility of compressing ROI with high fidelity up
to lossless and at early stages of the decoding process.
This allows receiving ROI data with high fidelity at
low bit rates while transmitting over PACS network.
The results show that proper selection of reversible
wavelet transforms is a significant element in ROI
image coding. All Mammograms are from MIAS
database of which we included normal mammograms
and abnormal ones with various types of