DATA COMPRESSION WITH APPLICATIONS TO DIGITAL RADIOLOGY

The structure of arithmetic codes is defined in terms of source parsing trees. The theoretical derivations of algorithms for the construction of optimal and sub-optimal structures are presented. The software simulation results demonstrate how arithmetic coding outperforms variable-length to variable-length coding.

Linear predictive coding is presented for the compression of digital diagnostic images from several imaging modalities including computed tomography, nuclear medicine, ultrasound, and magnetic resonance imaging. The problem of designing optimal predictors is formulated and alternative solutions are discussed. The results indicate that noiseless compression factors between 1.7 and 7.4 can be achieved.

With nonlinear predictive coding, noisy and noiseless compression techniques are combined in a novel way that may have a potential impact on picture archiving and communication systems in radiology. Adaptive fast discrete cosine transform coding systems are used as nonlinear block predictors, and optimal delta modulation systems are used as nonlinear sequential predictors. The off-line storage requirements for archiving diagnostic images are reasonably reduced by the nonlinear block predictive coding. The online performance, however, seems to be bounded by that of the linear systems. The subjective quality of image imperfect reproductions from the cosine transform coding is promising and prompts future research on the compression of diagnostic images by transform coding systems and the clinical evaluation of these systems.