Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2017  |  Volume : 8  |  Issue : 1  |  Page : 39

Impact of altering various image parameters on human epidermal growth factor receptor 2 image analysis data quality

1 Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
2 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
3 School of Information Science and Engineering, Xiamen University, Xiamen, China
4 Department of Biomedical Engineering; Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA

Correspondence Address:
Liron Pantanowitz
Department of Pathology, UPMC Cancer Pavilion, Suite 201, 5150 Centre Avenue, Pittsburgh, PA 15232
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_46_17

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Introduction: The quality of data obtained from image analysis can be directly affected by several preanalytical (e.g., staining, image acquisition), analytical (e.g., algorithm, region of interest [ROI]), and postanalytical (e.g., computer processing) variables. Whole-slide scanners generate digital images that may vary depending on the type of scanner and device settings. Our goal was to evaluate the impact of altering brightness, contrast, compression, and blurring on image analysis data quality. Methods: Slides from 55 patients with invasive breast carcinoma were digitized to include a spectrum of human epidermal growth factor receptor 2 (HER2) scores analyzed with Visiopharm (30 cases with score 0, 10 with 1+, 5 with 2+, and 10 with 3+). For all images, an ROI was selected and four parameters (brightness, contrast, JPEG2000 compression, out-of-focus blurring) then serially adjusted. HER2 scores were obtained for each altered image. Results: HER2 scores decreased with increased illumination, higher compression ratios, and increased blurring. HER2 scores increased with greater contrast. Cases with HER2 score 0 were least affected by image adjustments. Conclusion: This experiment shows that variations in image brightness, contrast, compression, and blurring can have major influences on image analysis results. Such changes can result in under- or over-scoring with image algorithms. Standardization of image analysis is recommended to minimize the undesirable impact such variations may have on data output.

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