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 as soon as Postprint is submitted to ZB.
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.
Altmetric
Additional Metrics?
Publication type
Article: Journal article
Document type
Scientific Article
Thesis type
Editors
Corresponding Author
Keywords
Image Processing ; Image Restoration ; Laser Scanning Microscopy
Keywords plus
ISSN (print) / ISBN
0018-9294
e-ISSN
0096-0616
ISBN
Book Volume Title
Conference Title
Conference Date
Conference Location
Proceedings Title
Quellenangaben
Volume: 67,
Issue: 1,
Pages: 79-87
Article Number: ,
Supplement: ,
Series
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place
New York, NY
University
University place
Faculty
Publication date
0000-00-00
Application date
0000-00-00
Patent owner
Further owners
Application country
Patent priority
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
Grants