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Image-based artefact removal in laser scanning microscopy

Research output: Contribution to journalArticle

Bartlomiej W. Papiez, Bostjan Markelc, Graham Brown, Ruth J. Muschel, M Brady, Julia A. Schnabel

Original languageEnglish
Article number8701671
Pages (from-to)79-87
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number1
Early online date29 Apr 2019
Publication statusPublished - Jan 2020


  • Image-based artefact removal_PAPIEZ_Accepted23March2019_GOLD AAM

    Image_based_artefact_removal_PAPIEZ_Accepted23March2019_GREEN_AAM.pdf, 11.8 MB, application/pdf


    Accepted author manuscript

    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

  • Image-based artefact removal_PAPIEZ_Accepted23March2019_GOLD VoR

    08701671.pdf, 6.6 MB, application/pdf


    Final published version

    CC BY

King's Authors


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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