Compression of Medical Images using Progressive Coders

Abstract

We have considered, in this paper, two compression methods based on wavelet transform and progressive coding, called EZW (Embedded Zerotree Wavelet) and SPIHT (Set partitioning in hierarchical trees). These techniques provide the opportunity to significantly increase compression ratios while preserving high qualities of reconstructed medical images. Unlike the EZW, the SPIHT approach takes advantage of subband correlations at near and far resolution levels (father-son relationships). A partial ordering by amplitude of the wavelet coefficients of the DWT (Discrete Wavelet Transform), partitioning in hierarchical trees, and scheduling of the transmission of the refinement bits are the three ideas used by SPIHT (the amplitude of each significant coefficient is progressively refined). The goal is to create a digital tool with the required main compression rate and PSNR (Peak Signal to Noise Ratio) limitations for compressing medical images. A development of the two cited algorithms has been carried out. For their evaluation, several medical images from two databases were used. The results prove that these algorithms are very efficient. Also, it has been found that the obtained results with SPIHT method greatly exceed those provided by EZW approach. SPIHT approach is very efficient compared to EZW from the point of view of the compression ratio and the image quality.