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Thoracic CT images: effect of lossy image compression on diagnostic accuracy
P C Cosman1, H C Davidson, C J Bergin
1Department of Electrical Engineering, Stanford University, CA 94305-4055.
Purpose:
To evaluate the effects of lossy image (noninvertible) compression on diagnostic accuracy of thoracic computed tomographic images.
Materials And Methods:
Sixty images from patients with mediastinal adenopathy and pulmonary nodules were compressed to six different levels with tree-structured vector quantization. Three radiologists then used the original and compressed images for diagnosis. Unlike many previous receiver operating characteristic-based studies that used confidence rankings and binary detection tasks, this study examined the sensitivity and predictive value positive scores from nonbinary detection tasks.
Results:
At the 5% significance level, there was no statistically significant difference in diagnostic accuracy of image assessment at compression rates of up to 9:1.
Conclusion:
The techniques presented for evaluation of image quality do not depend on the specific compression algorithm and provide a useful approach to evaluation of the benefits of any lossy image processing technique.
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