Evaluating quality of compressed medical images: SNR, subjectiverating, and diagnostic accuracy
Cosman, P.C.; Gray, R.M.; Olshen, R.A.
Proceedings of the IEEE
Volume 82, Issue 6, Jun 1994 Page(s):919 - 932
Digital Object Identifier 10.1109/5.286196
Summary: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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