Wavelet-based lossy-to-lossless medical image compression using dynamic VQ and SPIHT coding

As the coming era of digitized medical information, a close-at-hand challenge to deal with is the storage and transmission requirement of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose a dynamic vector quantization (DVQ) scheme with distortion-constrained codebook replenishment (DCCR) mechanism in wavelet domain. In the DVQ-DCCR mechanism, a novel tree-structure vector and the well-known SPIHT technique are combined to provide excellent coding performance in terms of compression ratio and peak signal-to-noise ratio for lossy compression. For the lossless compression in similar scheme, we replace traditional 9/7 wavelet filters by 5/3 filters and implement the wavelet transform in the lifting structure. Furthermore, a detection strategy is proposed to stop the SPIHT coding for
less significant bit planes, where SPIHT begins to lose its coding efficiency. Experimental results show that the proposed algorithm is superior to SPIHT with the arithmetic coding in both lossy and lossless compression for all tested images.