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首页> 外文期刊>Proceedings of the IEEE >Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy
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Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy

机译：评估压缩医学图像的质量：SNR，主观评分和诊断准确性

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摘要

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.
机译：压缩数字图像可以促进其传输，存储和处理。随着放射科数字化的发展，其影像数据的数量正迫使人们考虑在图片存档和通信系统（PACS）和不断发展的远程放射学系统中进行压缩。仅通过有损算法才能实现明显的压缩，而有损算法无法准确恢复原始图像。由于潜在的质量损失和随之而来的有关责任的问题，这种信息丢失使压缩和其他图像处理算法引起争议，但是必须考虑采用该技术，因为另一种选择是延迟，损坏以及图像的通信和召回丢失。如何确定图像是否足以满足特定应用的需求，例如诊断，召回，存档或教育用途？作者介绍了三种测量医学图像质量的方法：信噪比（SNR），主观评分和诊断准确性。他们在特定应用中比较和对比了这些措施，在某种程度上深入考虑了最近开发的用于确定有损压缩医学图像的诊断准确性的方法，并检查了像SNR这样容易获得的失真措施在预测更昂贵的主观和诊断等级方面的表现。这些示例是使用预测性修剪树状结构矢量量化压缩的医学图像，但是这些方法可用于产生与原始图像不同的任何数字图像处理以进行评估。

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