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An Objective Scoring Framework for Histology Slide Image Mosaics Applicable for the Reliable Benchmarking of Image Quality Assessment Algorithms
Politehnica University of Bucharest.
Politehnica University of Bucharest.
Carol Davila University of Medicine and Pharmacy, Bucharest.
Carol Davila University of Medicine and Pharmacy, Bucharest.
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2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 53080-53091Article in journal (Refereed) Published
Abstract [en]

The conversion of histology slides into electronic format represents a key element in modern histopathology workflows. The most common way of converting physical histology slides into digital versions consists of tile-based scanning. In such approaches, the entire image of the slide is generated by consecutively scanning adjacent sample regions with a degree of overlap and then stitching these together to constitute an image mosaic. To achieve a high-quality result, the image acquisition protocol for collecting the mosaic tiles requires a recalibration of the microscope when moving from one sample region to another. This recalibration procedure typically involves focus and illumination adjustments, aimed at rendering a homogeneous image mosaic in terms of brightness, contrast, and other important image properties. The accurate evaluation of the digital slide's quality factor is, therefore, an important matter, as it can lead to designing efficient (and automated) mosaic generation protocols. We introduce here a new methodology for the evaluation of image mosaics collected with brightfield microscopy on histology slides, coined Objective Quantifiable Scoring System (OQSS). It relies on objective scoring criteria that take into consideration fundamental characteristics of image mosaics, and on histology specific aspects. We present the theoretical principles of this methodology and discuss the potential utility of this framework as a quality ground-truth tagging mechanism of histology slide image mosaics applicable for the reliable benchmarking of image quality assessment algorithms.

Place, publisher, year, edition, pages
IEEE, 2018. Vol. 6, p. 53080-53091
Keywords [en]
Microscopy, Benchmark testing, Image quality, Pathology, Reliability, Protocols, Lighting
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-367991DOI: 10.1109/ACCESS.2018.2868127ISI: 000447722200001OAI: oai:DiVA.org:uu-367991DiVA, id: diva2:1267369
Funder
VINNOVAAvailable from: 2018-12-02 Created: 2018-12-02 Last updated: 2018-12-10Bibliographically approved

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Sladoje, Natasa

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