Advanced search
2 files | 2.26 MB

Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization

Joris Roels (UGent) , Jan Aelterman (UGent) , Jonas De Vylder (UGent) , Hiep Luong (UGent) , Yvan Saeys (UGent) and Wilfried Philips (UGent)
Author
Organization
Abstract
Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps.
Keywords
:ABERRATION

Downloads

  • EMBC 2016.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 963.12 KB
  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.29 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Roels, Joris, Jan Aelterman, Jonas De Vylder, Hiep Luong, Yvan Saeys, and Wilfried Philips. 2016. “Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization.” In IEEE Engineering in Medicine and Biology Society Conference Proceedings, 443–447. New York, NY, USA: IEEE.
APA
Roels, Joris, Aelterman, J., De Vylder, J., Luong, H., Saeys, Y., & Philips, W. (2016). Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization. IEEE Engineering in Medicine and Biology Society Conference Proceedings (pp. 443–447). Presented at the 38th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), New York, NY, USA: IEEE.
Vancouver
1.
Roels J, Aelterman J, De Vylder J, Luong H, Saeys Y, Philips W. Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization. IEEE Engineering in Medicine and Biology Society Conference Proceedings. New York, NY, USA: IEEE; 2016. p. 443–7.
MLA
Roels, Joris, Jan Aelterman, Jonas De Vylder, et al. “Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization.” IEEE Engineering in Medicine and Biology Society Conference Proceedings. New York, NY, USA: IEEE, 2016. 443–447. Print.
@inproceedings{8506618,
  abstract     = {Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps.},
  author       = {Roels, Joris and Aelterman, Jan and De Vylder, Jonas and Luong, Hiep and Saeys, Yvan and Philips, Wilfried},
  booktitle    = {IEEE Engineering in Medicine and Biology Society Conference Proceedings},
  isbn         = {9781457702204},
  issn         = {1557-170X},
  keyword      = {:ABERRATION},
  language     = {eng},
  location     = {Orlando, FL, USA},
  pages        = {443--447},
  publisher    = {IEEE},
  title        = {Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization},
  url          = {http://dx.doi.org/10.1109/embc.2016.7590735},
  year         = {2016},
}

Altmetric
View in Altmetric
Web of Science
Times cited: