An Automated Blur Detection Method for Histological Whole Slide Imaging

Xavier Moles Lopez, Etienne D'Andrea, Paul Barbot, Anne-Sophie Bridoux, Sandrine Rorive, Isabelle Salmon, Olivier Debeir and Christine Decaestecker

PLOS ONE, 2013, vol. 8, issue 12, 1-11

Abstract: Whole slide scanners are novel devices that enable high-resolution imaging of an entire histological slide. Furthermore, the imaging is achieved in only a few minutes, which enables image rendering of large-scale studies involving multiple immunohistochemistry biomarkers. Although whole slide imaging has improved considerably, locally poor focusing causes blurred regions of the image. These artifacts may strongly affect the quality of subsequent analyses, making a slide review process mandatory. This tedious and time-consuming task requires the scanner operator to carefully assess the virtual slide and to manually select new focus points. We propose a statistical learning method that provides early image quality feedback and automatically identifies regions of the image that require additional focus points.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0082710

DOI: 10.1371/journal.pone.0082710

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