OBJECTIVE: Image data compression is an enabling technology for teleradiology and picture archive and communication systems. Compression decreases the time and cost of image transmission and the requirements for image storage. Wavelets, discovered in 1987, constitute a new compression technique that has been described in radiologic publications but, to our knowledge, no previous studies of its use have been reported. The purpose of this study was to demonstrate the application of wavelet-based compression technology to digitized radiographs. MATERIALS AND METHODS: Twelve radiographs with abnormal findings were digitized, compressed, and decompressed by using a new wavelet-based lossy compression algorithm. Images were compressed at ratios from 10:1 to 60:1. Seven board-certified radiologists reviewed images on a two-headed, high-resolution (2K x 2K) diagnostic workstation. Paired original and compressed/decompressed images were presented in random order. Reviewers adjusted contrast and magnification to judge whether image degradation was present, and if so, whether it was of diagnostic significance. Quantitative error measures were tabulated. RESULTS: Reviewers found no clinically relevant degradation below a compression ratio of 30:1. Skeletal radiographs appeared more sensitive to compression than did chest or abdominal radiographs, but the trend was not statistically significant. Quantitative error measures increased gradually with compression ratio. CONCLUSION: On the basis of subjective assessment of image quality and the computational efficiency of the algorithm, wavelet-base techniques appear promising for the compression of digitized radiographs. The results of this initial experience can be used to design appropriate observer performance studies.
OBJECTIVE: Image data compression is an enabling technology for teleradiology and picture archive and communication systems. Compression decreases the time and cost of image transmission and the requirements for image storage. Wavelets, discovered in 1987, constitute a new compression technique that has been described in radiologic publications but, to our knowledge, no previous studies of its use have been reported. The purpose of this study was to demonstrate the application of wavelet-based compression technology to digitized radiographs. MATERIALS AND METHODS: Twelve radiographs with abnormal findings were digitized, compressed, and decompressed by using a new wavelet-based lossy compression algorithm. Images were compressed at ratios from 10:1 to 60:1. Seven board-certified radiologists reviewed images on a two-headed, high-resolution (2K x 2K) diagnostic workstation. Paired original and compressed/decompressed images were presented in random order. Reviewers adjusted contrast and magnification to judge whether image degradation was present, and if so, whether it was of diagnostic significance. Quantitative error measures were tabulated. RESULTS: Reviewers found no clinically relevant degradation below a compression ratio of 30:1. Skeletal radiographs appeared more sensitive to compression than did chest or abdominal radiographs, but the trend was not statistically significant. Quantitative error measures increased gradually with compression ratio. CONCLUSION: On the basis of subjective assessment of image quality and the computational efficiency of the algorithm, wavelet-base techniques appear promising for the compression of digitized radiographs. The results of this initial experience can be used to design appropriate observer performance studies.
Authors: S Terae; K Miyasaka; K Kudoh; T Nambu; T Shimizu; K Kaneko; H Yoshikawa; R Kishimoto; T Omatsu; N Fujita Journal: J Digit Imaging Date: 2000-11 Impact factor: 4.056