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1 July 1990 Lossy image compression for digital medical imaging systems
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Image compression at rates of 10:1 or greater could make PACS much more responsive and economically attractive. This paper describes a protocol for subjective and objective evaluation of the fidelity of compressed/decompressed images to the originals and presents the results ofits application to four representative and promising compression methods. The methods examined are predictive pruned tree-structured vector quantization, fractal compression, the discrete cosine transform with equal weighting of block bit allocation, and the discrete cosine transform with human visual system weighting of block bit allocation. Vector quantization is theoretically capable of producing the best compressed images, but has proven to be difficult to effectively implement. It has the advantage that it can reconstruct images quickly through a simple lookup table. Disadvantages are that codebook training is required, the method is computationally intensive, and achieving the optimum performance would require prohibitively long vector dimensions. Fractal compression is a relatively new compression technique, but has produced satisfactory results while being computationally simple. It is fast at both image compression and image reconstruction. Discrete cosine iransform techniques reproduce images well, but have traditionally been hampered by the need for intensive computing to compress and decompress images. A protocol was developed for side-by-side observer comparison of reconstructed images with originals. Three 1024 X 1024 CR (Computed Radiography) images and two 512 X 512 X-ray CT images were viewed at six bit rates (0.2, 0.4, 0.6, 0.9, 1.2, and 1.5 bpp for CR, and 1.0, 1.3, 1.6, 1.9, 2.2, 2.5 bpp for X-ray CT) by nine radiologists at the University of Washington Medical Center. The CR images were viewed on a Pixar II Megascan (2560 X 2048) monitor and the CT images on a Sony (1280 X 1024) monitor. The radiologists' subjective evaluations of image fidelity were compared to calculations of mean square error (MSE), normalized mean square error (NMSE), percentage mean square error (PMSE), and fractal normalized mean square error (FMSE) for each compression method and bit rate.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Paul S. Wilhelm, David R. Haynor, Yongmin Kim, Alan C. Nelson, and Eve A. Riskin "Lossy image compression for digital medical imaging systems", Proc. SPIE 1232, Medical Imaging IV: Image Capture and Display, (1 July 1990);

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