The objective of radiologic image compression is to reduce the data volume of and to achieve a low bit rate in the digital representation of radiologic images without perceived loss of image quality. However, the demand for transmission bandwidth and storage space in the digital radiology environment, especially picture archiving and communication systems (PACS) and teleradiology, and the proliferating use of various imaging modalities, such as magnetic resonance imaging, computed tomography, ultrasonography, nuclear medicine, computed radiography, and digital subtraction angiography, continue to outstrip the capabilities of existing technologies. The availability of lossy coding techniques for clinical diagnoses further implicates many complex legal and regulatory issues. This paper reviews the recent progress of lossless and lossy radiologic image compression and presents the legal challenges of using lossy compression of medical records. To do so, we first describe the fundamental concepts of radiologic imaging and digitization. Then, we examine current compression technology in the field of medical imaging and discuss important regulatory policies and legal questions facing the use of compression in this field. We conclude with a summary of future challenges and research directions. >

Radiologic image compression-a review

Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities fo their imaging data are forcing consideration of compression in picture archiving and communication systems. Significant compression is achievable anly by lossy algorithms, which do not permit the exact recovery of the original images

Evaluating quality of compressed medical images : SNR, subjective rating, and diagnostic accuracy : Data compression

Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities of their imaging data are forcing consideration of compression in picture archiving and communication systems (PACS) and evolving teleradiology systems. Significant compression is achievable only by lossy algorithms, which do not permit the exact recovery of the original image. This loss of information renders compression and other image processing algorithms controversial because of the potential loss of quality and consequent problems regarding liability, but the technology must be considered because the alternative is delay, damage, and loss in the communication and recall of the images. How does one decide if an image is good enough for a specific application, such as diagnosis, recall, archival, or educational use? The authors describe three approaches to the measurement of medical image quality: signal-to-noise ratio (SNR), subjective rating, and diagnostic accuracy. They compare and contrast these measures in a particular application, consider in some depth recently developed methods for determining diagnostic accuracy of lossy compressed medical images and examine how good the easily obtainable distortion measures like SNR are at predicting the more expensive subjective and diagnostic ratings. The examples are of medical images compressed using predictive pruned tree-structured vector quantization, but the methods can be used for any digital image processing that produces images different from the original for evaluation. >

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Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy

The performances of a number of block-based, reversible, compression algorithms suitable for compression of very-large-format images (4096*4096 pixels or more) are compared to that of a novel two-dimensional linear predictive coder developed by extending the multichannel version of the Burg algorithm to two dimensions. The compression schemes implemented are: Huffman coding, Lempel-Ziv coding, arithmetic coding, two-dimensional linear predictive coding (in addition to the aforementioned one), transform coding using discrete Fourier-, discrete cosine-, and discrete Walsh transforms, linear interpolative coding, and combinations thereof. The performances of these coding techniques for a few mammograms and chest radiographs digitized to sizes up to 4096*4096 10 b pixels are discussed. Compression from 10 b to 2.5-3.0 b/pixel on these images has been achieved without any loss of information. The modified multichannel linear predictor outperforms the other methods while offering certain advantages in implementation. >

Performance analysis of reversible image compression techniques for high-resolution digital teleradiology

Three techniques for variable-rate vector quantizer design are applied to medical images. The first two are extensions of an algorithm for optimal pruning in tree-structured classification and regression due to Breiman et al. The code design algorithms find subtrees of a given tree-structured vector quantizer (TSVQ), each one optimal in that it has the lowest average distortion of all subtrees of the TSVQ with the same or lesser average rate. Since the resulting subtrees have variable depth, natural variable-rate coders result. The third technique is a joint optimization of a vector quantizer and a noiseless variable-rate code. This technique is relatively complex but it has the potential to yield the highest performance of all three techniques. >

Variable rate vector quantization for medical image compression

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.

Lossy image compression for digital medical imaging systems

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.

Paul S. Wilhelm

Papers

Lossy image compression for digital medical imaging systems

Lossy image compression for digital medical imaging systems