Title :
Near-lossless compression of medical images through entropy-coded DPCM
Author :
Chen, Keshi ; Ramabadran, Tenkasi V.
Author_Institution :
Dept. of Electr. Eng. & Comput. Eng., Iowa State Univ., Ames, IA, USA
fDate :
9/1/1994 12:00:00 AM
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
Keywords :
biomedical NMR; biomedical ultrasonics; data compression; medical image processing; pulse-code modulation; 7 to 8 dB; 8-bit medical images; JPEG standard; arithmetic coding; diagnostic images; entropy-coded DPCM method; high-fidelity compression; magnetic resonance images; medical image compression; multiple contexts; near-lossless compression; source model; ultrasound images; Arithmetic; Biomedical imaging; Context modeling; Image coding; Image reconstruction; Image storage; Medical diagnostic imaging; Predictive models; Quantization; Transform coding;
Journal_Title :
Medical Imaging, IEEE Transactions on
