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Compressing pathology whole-slide images using a human and model observer evaluation.

Elizabeth A Krupinski1, Jeffrey P Johnson, Stacey Jaw

  • 1Department of Medical Imaging, University of Arizona, 1609 N. Warren, Tucson, AZ 85724, USA.

Journal of Pathology Informatics
|May 23, 2012
PubMed
Summary

Whole-slide images can be compressed significantly without affecting pathologist accuracy in distinguishing benign from malignant tissues. A visual discrimination model effectively predicts performance impacts from compression artifacts.

Keywords:
Compressionhuman visual system discrimination modelobserver performancepathology whole slide images

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Area of Science:

  • Digital Pathology
  • Medical Imaging
  • Image Compression

Background:

  • Whole-slide images (WSI) are crucial for pathological diagnosis.
  • Assessing the impact of image compression on diagnostic accuracy is vital for efficient data management.

Purpose of the Study:

  • To determine the maximum compression level for WSI without compromising diagnostic performance.
  • To evaluate the utility of a visual discrimination model (VDM) in predicting pathologist performance under compression.

Main Methods:

  • Six pathologists evaluated 100 breast biopsy WSI regions of interest at various JPEG 2000 compression ratios (8:1 to 128:1).
  • A VDM was used to predict artifact visibility and just-noticeable difference (JND) metrics.
  • Standard metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were calculated for comparison.

Main Results:

  • Pathologist performance (ROC Az) remained stable up to a 32:1 compression ratio, then significantly declined at 64:1 and 128:1.
  • VDM metrics, including JND, correlated well with observed artifact conspicuity.
  • Image fidelity metrics (PSNR, SSIM) decreased with increasing compression, as expected.

Conclusions:

  • WSI can tolerate relatively high compression levels without negatively impacting diagnostic interpretation.
  • VDM-derived metrics show promise for predicting image quality and observer performance in digital pathology.