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Image degradation in microscopic images : avoidance, artifacts and solutions

Joris Roels (UGent) , Jan Aelterman (UGent) , Jonas De Vylder (UGent) , Saskia Lippens (UGent) , Hiep Luong (UGent) , Chris Guerin (UGent) and Wilfried Philips (UGent)
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Abstract
The goal of modern microscopy is to acquire high-quality image based data sets. A typical microscopy workflow is set up in order to address a specific biological question and involves different steps. One has to first precisely define the biological question, in order to properly come to an experimental design for sample preparation and image acquisition. A better object representation allows biological users to draw more reliable scientific conclusions. Image restoration can manipulate the acquired data in an effort to reduce the impact of artifacts (spurious results) due to physical and technical limitations, resulting in a better representation of the object of interest. However, precise usage of these algorithms is necessary in order to not introduce further artifacts that might influence the data-analysis and bias the conclusions. It is essential to understand image acquisition, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. This paper provides an overview of the fundamental artifacts and degradations that affect many micrographs. We describe why artifacts appear, in what sense they impact overall image quality and how to mitigate them by first improving the acquisition parameters and then applying proper image restoration techniques.
Keywords
Non-uniform illumination, Digitization, Light microscopy, Compression, Electron Microscopy, Image restoration, Microscopy image degradations, Microscopy, Noise, Blur, SIGNAL-DEPENDENT NOISE, WAVELET DOMAIN, LIGHT-MICROSCOPY, EXTENDED DEPTH, DECONVOLUTION, FUSION, FIELD, MINIMIZATION, RESTORATION, ALGORITHM

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Chicago
Roels, Joris, Jan Aelterman, Jonas De Vylder, Saskia Lippens, Hiep Luong, Chris Guerin, and Wilfried Philips. 2016. “Image Degradation in Microscopic Images : Avoidance, Artifacts and Solutions.” Ed. Winnok De Vos, Sebastian Munck, and Jean-Pierre Timmermans. Advances in Anatomy Embryology and Cell Biology 219: 41–67.
APA
Roels, Joris, Aelterman, J., De Vylder, J., Lippens, S., Luong, H., Guerin, C., & Philips, W. (2016). Image degradation in microscopic images : avoidance, artifacts and solutions. (Winnok De Vos, S. Munck, & J.-P. Timmermans, Eds.)Advances in Anatomy Embryology and Cell Biology, 219, 41–67.
Vancouver
1.
Roels J, Aelterman J, De Vylder J, Lippens S, Luong H, Guerin C, et al. Image degradation in microscopic images : avoidance, artifacts and solutions. De Vos W, Munck S, Timmermans J-P, editors. Advances in Anatomy Embryology and Cell Biology. Berlin, Germany: Springer; 2016;219:41–67.
MLA
Roels, Joris, Jan Aelterman, Jonas De Vylder, et al. “Image Degradation in Microscopic Images : Avoidance, Artifacts and Solutions.” Ed. Winnok De Vos, Sebastian Munck, & Jean-Pierre Timmermans. Advances in Anatomy Embryology and Cell Biology 219 (2016): 41–67. Print.
@article{6965914,
  abstract     = {The goal of modern microscopy is to acquire high-quality image based data sets. A typical microscopy workflow is set up in order to address a specific biological question and involves different steps. One has to first precisely define the biological question, in order to properly come to an experimental design for sample preparation and image acquisition. A better object representation allows biological users to draw more reliable scientific conclusions. Image restoration can manipulate the acquired data in an effort to reduce the impact of artifacts (spurious results) due to physical and technical limitations, resulting in a better representation of the object of interest. However, precise usage of these algorithms is necessary in order to not introduce further artifacts that might influence the data-analysis and bias the conclusions. It is essential to understand image acquisition, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. This paper provides an overview of the fundamental artifacts and degradations that affect many micrographs. We describe why artifacts appear, in what sense they impact overall image quality and how to mitigate them by first improving the acquisition parameters and then applying proper image restoration techniques.},
  author       = {Roels, Joris and Aelterman, Jan and De Vylder, Jonas and Lippens, Saskia and Luong, Hiep and Guerin, Chris and Philips, Wilfried},
  editor       = {De Vos, Winnok and Munck, Sebastian and Timmermans, Jean-Pierre},
  isbn         = {9783319285474},
  issn         = {0301-5556},
  journal      = {Advances in Anatomy Embryology and Cell Biology},
  keyword      = {Non-uniform illumination,Digitization,Light microscopy,Compression,Electron Microscopy,Image restoration,Microscopy image degradations,Microscopy,Noise,Blur,SIGNAL-DEPENDENT NOISE,WAVELET DOMAIN,LIGHT-MICROSCOPY,EXTENDED DEPTH,DECONVOLUTION,FUSION,FIELD,MINIMIZATION,RESTORATION,ALGORITHM},
  language     = {eng},
  pages        = {41--67},
  publisher    = {Springer},
  title        = {Image degradation in microscopic images : avoidance, artifacts and solutions},
  url          = {http://dx.doi.org/10.1007/978-3-319-28549-8\_2},
  volume       = {219},
  year         = {2016},
}

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