ArticleIEEE transactions on medical imaging2011
Using a visual discrimination model for the detection of compression artifacts in virtual pathology images.
Article in IEEE transactions on medical imaging, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Unlocking the potential of digital pathology: Novel baselines for compression.Journal of pathology informatics · 2025Article
- Lossy Image Compression in a Preclinical Multimodal Imaging Study.Journal of digital imaging · 2023Article
- Video compression to support the expansion of whole-slide imaging into cytology.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Structural similarity index family for image quality assessment in radiological images.Journal of medical imaging (Bellingham, Wash.) · 2017Article
- Method for Adapting the Grayscale Standard Display Function to the Aging Eye.Journal of digital imaging · 2017Article
- Staining correction in digital pathology by utilizing a dye amount table.Journal of digital imaging · 2015Article
- The history of pathology informatics: A global perspective.Journal of pathology informatics · 2013Article
- Compressing pathology whole-slide images using a human and model observer evaluation.Journal of pathology informatics · 2012Article
- Comment on "Quality evaluation of microscopy and scanned histological images for diagnostic purposes": Are scanners better than microscopes?Journal of pathology informatics · 2012Article
- Using a visual discrimination model for the detection of compression artifacts in virtual pathology images.IEEE transactions on medical imaging · 2011Article
- Assessment of Boundary Discrimination Performance in a Printed Phantom.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
6 authors.
Funding
Abstract
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 5-12 times the data reduction of reversible methods.
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