An evaluation of 3-D vs. 2-D compression of medical images with set partitioning in hierarchical trees

Wavelet-based image compression is a very effective technique for medical images, giving significantly better results than the JPEG algorithm. Recent advanced wavelet techniques, such as embedded zerotree wavelet coding or set partitioning in hierarchical trees (SPIHT), have further improved compression efficency by exploiting the natural relationship between corresponding coefficients at different scales and by progressively refining coefficient values. It is also well known that full 3-D wavelet compression of 3-D data sets is significantly more efficient than 2-D compression of individual slices, but the memory requirements are very high. We explore the extension of the SPIHT algorithm to three dimensions, and also of intermediate approaches such as the encoding of a 3-D image in "slabs" of 16 slices at a time or simple subtraction of neighboring slices, which yield different tradeoffs between speed and memory requirements on the one hand and compression efficiency on the other for a variety of possible approaches on typical 3-D CT and MRI data sets.