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Papiez, B.W.* ; Markelc, B.* ; Brown, G.D.* ; Muschel, R.J.* ; Brady, S.M.* ; Schnabel, J.A.*

Image-based artefact removal in laser scanning microscopy.

IEEE Trans. Bio. Med. Eng. 67, 79-87 (2020)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Recent developments in laser scanning microscopy have greatly extended its applicability in cancer imaging beyond the visualization of complex biology, and opened up the possibility of quantitative analysis of inherently dynamic biological processes. However, the physics of image acquisition intrinsically means that image quality is subject to a tradeoff between a number of imaging parameters, including resolution, signal-to-noise ratio, and acquisition speed. We address the problem of geometric distortion, in particular, jaggedness artefacts that are caused by the variable motion of the microscope laser, by using a combination of image processing techniques. Image restoration methods have already shown great potential for post-acquisition image analysis. The performance of our proposed image restoration technique was first quantitatively evaluated using phantom data with different textures, and then qualitatively assessed using in vivo biological imaging data. In both cases, the presented method, comprising a combination of image registration and filtering, is demonstrated to have substantial improvement over state-of-the-art microscopy acquisition methods.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Image Processing ; Image Restoration ; Laser Scanning Microscopy
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 0018-9294
e-ISSN 0096-0616
Zeitschrift IEEE Transactions on Bio-Medical Electronics
Quellenangaben Band: 67, Heft: 1, Seiten: 79-87 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
DOI 10.1109/TBME.2019.2908345
Scopus ID 85077174126
PubMed ID 31034401
Erfassungsdatum 2022-09-07
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