TY - JOUR
T1 - Using a visual discrimination model for the detection of compression artifacts in virtual pathology images
AU - Johnson, Jeffrey P.
AU - Krupinski, Elizabeth A.
AU - Yan, Michelle
AU - Roehrig, Hans
AU - Graham, Anna R.
AU - Weinstein, Ronald S.
N1 - Funding Information:
Manuscript received July 23, 2010; accepted September 08, 2010. Date of publication September 23, 2010; date of current version February 02, 2011. This work was supported by National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering (NIH/NIBIB) under Grant R01 EB008055. Asterisk indicates corresponding author.
PY - 2011/2
Y1 - 2011/2
N2 - A major issue in telepathology is the extremely large and growing size of digitized virtual slides, which can require several gigabytes of storage and cause significant delays in data transmission for remote image interpretation and interactive visualization by pathologists. Compression can reduce this massive amount of virtual slide data, but reversible (lossless) methods limit data reduction to less than 50%, while lossy compression can degrade image quality and diagnostic accuracy. Visually lossless compression offers the potential for using higher compression levels without noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility. We investigated the utility of a visual discrimination model (VDM) and other distortion metrics for predicting JPEG 2000 bit rates corresponding to visually lossless compression of virtual slides for breast biopsy specimens. Threshold bit rates were determined experimentally with human observers for a variety of tissue regions cropped from virtual slides. For test images compressed to their visually lossless thresholds, just-noticeable difference (JND) metrics computed by the VDM were nearly constant at the 95th percentile level or higher, and were significantly less variable than peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. Our results suggest that VDM metrics could be used to guide the compression of virtual slides to achieve visually lossless compression while providing 512 times the data reduction of reversible methods.
AB - A major issue in telepathology is the extremely large and growing size of digitized virtual slides, which can require several gigabytes of storage and cause significant delays in data transmission for remote image interpretation and interactive visualization by pathologists. Compression can reduce this massive amount of virtual slide data, but reversible (lossless) methods limit data reduction to less than 50%, while lossy compression can degrade image quality and diagnostic accuracy. Visually lossless compression offers the potential for using higher compression levels without noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility. We investigated the utility of a visual discrimination model (VDM) and other distortion metrics for predicting JPEG 2000 bit rates corresponding to visually lossless compression of virtual slides for breast biopsy specimens. Threshold bit rates were determined experimentally with human observers for a variety of tissue regions cropped from virtual slides. For test images compressed to their visually lossless thresholds, just-noticeable difference (JND) metrics computed by the VDM were nearly constant at the 95th percentile level or higher, and were significantly less variable than peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. Our results suggest that VDM metrics could be used to guide the compression of virtual slides to achieve visually lossless compression while providing 512 times the data reduction of reversible methods.
KW - Compression
KW - just noticeable differences
KW - virtual pathology slides
KW - visual discrimination model
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U2 - 10.1109/TMI.2010.2077308
DO - 10.1109/TMI.2010.2077308
M3 - Article
C2 - 20875970
AN - SCOPUS:79551600371
SN - 0278-0062
VL - 30
SP - 306
EP - 314
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 2
M1 - 5582290
ER -