No abstract

No abstract

The near-lossless, i.e., lossy but high-fidelity, compression of medical Images using the entropy-coded DPCM method is investigated. A source model with multiple contexts and arithmetic coding are used to enhance the compression performance of the method. In implementing the method, two different quantizers each with a large number of quantization levels are considered. Experiments involving several MR (magnetic resonance) and US (ultrasound) images show that the entropy-coded DPCM method can provide compression in the range from 4 to 10 with a peak SNR of about 50 dB for 8-bit medical images. The use of multiple contexts is found to improve the compression performance by about 25% to 30% for MR images and 30% to 35% for US images. A comparison with the JPEG standard reveals that the entropy-coded DPCM method can provide about 7 to 8 dB higher SNR for the same compression performance.

The authors investigate the use of conditioning events (or contexts) in improving the performances of known compression methods by building a source model with multiple contexts to code the decorrelated pixels. Three methods for reversible compression, namely DPCM (differential pulse code modulation), WHT (Walsh-Hadamard transform), and HINT (hierarchical interpolation), employing, respectively, predictive decorrelation, transform decorrelation, and multiresolution decorrelation, are considered. It is shown that the performance of these methods can be enhanced significantly, sometimes even up to 40%, by using contexts. The enhanced DPCM method is found to perform the best for MR and UT (ultrasound) medical images; the enhanced WHT method is found to be the best for X-ray images. The source models used in the enhanced models employ several hundred contexts.

No abstract