Share Email Print

Optical Engineering

Lossy image compression for digital medical imaging systems
Author(s): Paul S. Wilhelm; David R. Haynor; Yongmin Kim; Eve A. Riskin
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Image compression at rates oflO:1 orgreatercould make picture archiving and communication systems (PACS) much more responsive and economically attractive. A protocol is described for subjective and objective evaluation of the fidelity of compressed/decompressed images compared to originals. The results of its application to four representative and promising compression methods are presented. The four compression methods examined are predictive pruned tree-structured vector quantization, fractal compression, the full-frame discrete cosine transform with equal weighting of block bit allocation, and the full-frame discrete cosine transform with human visual system weighting of block bit allocation. A protocol was developed for side-by-side observer comparison of reconstructed images with originals. Three 1024 x 1024 computed radiography (CR) images andtwo 512 x 512 x-ray computed tomography(CT) images were viewed at six bit rates by nine radiologists at the University of Washington Medical Center. The radiologists' subjective evaluations of image fidelity were compared to calculations of mean square error for each decompressed image.

Paper Details

Date Published: 1 October 1991
PDF: 7 pages
Opt. Eng. 30(10) doi: 10.1117/12.55970
Published in: Optical Engineering Volume 30, Issue 10
Show Author Affiliations
Paul S. Wilhelm
David R. Haynor, Univ. of Washington (United States)
Yongmin Kim, Univ. of Washington (United States)
Eve A. Riskin, Univ. of Washington (United States)

© SPIE. Terms of Use
Back to Top