A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Image Quality Ranking Method for Microscopy
Julkaisun tekijät: Koho S, Fazeli E, Eriksson JE, Hanninen PE
Kustantaja: NATURE PUBLISHING GROUP
Julkaisuvuosi: 2016
Journal: Scientific Reports
Tietokannassa oleva lehden nimi: SCIENTIFIC REPORTS
Lehden akronyymi: SCI REP-UK
Volyymi: 6
Sivujen määrä: 14
ISSN: 2045-2322
DOI: http://dx.doi.org/10.1038/srep28962
Tiivistelmä
Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.
Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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