Compressing pathology whole-slide images using a human and model observer evaluation
Introduction: We aim to determine to what degree whole-slide images (WSI) can be compressed without impacting the ability of the pathologist to distinguish benign from malignant tissues. An underlying goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer p...
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Wolters Kluwer Medknow Publications
2012-01-01
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doaj-76dd53a1b13f4e63b3ec2e35fb0fdc312020-11-24T23:20:26ZengWolters Kluwer Medknow PublicationsJournal of Pathology Informatics2153-35392153-35392012-01-0131171710.4103/2153-3539.95129Compressing pathology whole-slide images using a human and model observer evaluationElizabeth A KrupinskiJeffrey P JohnsonStacey JawAnna R GrahamRonald S WeinsteinIntroduction: We aim to determine to what degree whole-slide images (WSI) can be compressed without impacting the ability of the pathologist to distinguish benign from malignant tissues. An underlying goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer performance. Materials and Methods: A total of 100 regions of interest (ROIs) from a breast biopsy whole-slide images at five levels of JPEG 2000 compression (8:1, 16:1, 32:1, 64:1, and 128:1) plus the uncompressed version were shown to six pathologists to determine benign versus malignant status. Results: There was a significant decrease in performance as a function of compression ratio (F = 14.58, P < 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. Just-noticeable difference (JND) metrics were computed for each image, including the mean, median, ≥90th percentiles, and maximum values. For comparison, PSNR (peak signal-to-noise ratio) and Structural Similarity (SSIM) were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. Observer performance as measured by the Receiver Operating Characteristic area under the curve (ROC Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression levels. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99th percentile JND of 6.5. Conclusion: Whole-slide images may be compressible to relatively high levels before impacting WSI interpretation performance. The VDM metrics correlated well with artifact conspicuity and human performance.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2012;volume=3;issue=1;spage=17;epage=17;aulast=Compressionhuman visual system discrimination modelobserver performancepathology whole slide images |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Elizabeth A Krupinski Jeffrey P Johnson Stacey Jaw Anna R Graham Ronald S Weinstein |
spellingShingle |
Elizabeth A Krupinski Jeffrey P Johnson Stacey Jaw Anna R Graham Ronald S Weinstein Compressing pathology whole-slide images using a human and model observer evaluation Journal of Pathology Informatics Compression human visual system discrimination model observer performance pathology whole slide images |
author_facet |
Elizabeth A Krupinski Jeffrey P Johnson Stacey Jaw Anna R Graham Ronald S Weinstein |
author_sort |
Elizabeth A Krupinski |
title |
Compressing pathology whole-slide images using a human and model observer evaluation |
title_short |
Compressing pathology whole-slide images using a human and model observer evaluation |
title_full |
Compressing pathology whole-slide images using a human and model observer evaluation |
title_fullStr |
Compressing pathology whole-slide images using a human and model observer evaluation |
title_full_unstemmed |
Compressing pathology whole-slide images using a human and model observer evaluation |
title_sort |
compressing pathology whole-slide images using a human and model observer evaluation |
publisher |
Wolters Kluwer Medknow Publications |
series |
Journal of Pathology Informatics |
issn |
2153-3539 2153-3539 |
publishDate |
2012-01-01 |
description |
Introduction: We aim to determine to what degree whole-slide images (WSI) can be compressed without impacting the ability of the pathologist to distinguish benign from malignant tissues. An underlying goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer performance. Materials and Methods: A total of 100 regions of interest (ROIs) from a breast biopsy whole-slide images at five levels of JPEG 2000 compression (8:1, 16:1, 32:1, 64:1, and 128:1) plus the uncompressed version were shown to six pathologists to determine benign versus malignant status. Results: There was a significant decrease in performance as a function of compression ratio (F = 14.58, P < 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. Just-noticeable difference (JND) metrics were computed for each image, including the mean, median, ≥90th percentiles, and maximum values. For comparison, PSNR (peak signal-to-noise ratio) and Structural Similarity (SSIM) were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. Observer performance as measured by the Receiver Operating Characteristic area under the curve (ROC Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression levels. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99th percentile JND of 6.5. Conclusion: Whole-slide images may be compressible to relatively high levels before impacting WSI interpretation performance. The VDM metrics correlated well with artifact conspicuity and human performance. |
topic |
Compression human visual system discrimination model observer performance pathology whole slide images |
url |
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2012;volume=3;issue=1;spage=17;epage=17;aulast= |
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