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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Main Authors: Elizabeth A Krupinski, Jeffrey P Johnson, Stacey Jaw, Anna R Graham, Ronald S Weinstein
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2012-01-01
Series:Journal of Pathology Informatics
Subjects:
Online Access:http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2012;volume=3;issue=1;spage=17;epage=17;aulast=
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spelling 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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