Advanced search

From a biological sample to high-quality digital image representation: avoiding and restoring image artifacts through proper acquisition and image restoration

Joris Roels (UGent) , Jan Aelterman (UGent) , Jonas De Vylder (UGent) , Hiep Luong (UGent) , Saskia Lippens (UGent) , Chris Guerin (UGent) , Yvan Saeys (UGent) and Wilfried Philips (UGent)
(2016)
Author
Organization
Abstract
The objective 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: sample preparation, image acquisition, image storage, restoration (if necessary) and analysis. In order to analyze the images and draw scientific conclusions, it is crucial to obtain image data that reflects reality as close as possible. We provide an overview of the fundamental artifacts and degradations that affect many micrographs during the acquisition and storage phase: non-uniform illumination, blur, noise, digitization and compression. Image restoration techniques such as flat-field correction, deconvolution and denoising can manipulate the acquired data in an effort to reduce the impact of artifacts due to physical and technical limitations, resulting in a better representation (i.e. a more accurate correspondence to reality) 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. For example, high compression rates may result into small file sizes, but typically introduce blocking artifacts that might affect consecutive image segmentation. It is essential to understand image acquisition and storage, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. 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.

Citation

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

Chicago
Roels, Joris, Jan Aelterman, Jonas De Vylder, Hiep Luong, Saskia Lippens, Chris Guerin, Yvan Saeys, and Wilfried Philips. 2016. “From a Biological Sample to High-quality Digital Image Representation: Avoiding and Restoring Image Artifacts Through Proper Acquisition and Image Restoration.” In .
APA
Roels, Joris, Aelterman, J., De Vylder, J., Luong, H., Lippens, S., Guerin, C., Saeys, Y., et al. (2016). From a biological sample to high-quality digital image representation: avoiding and restoring image artifacts through proper acquisition and image restoration. Presented at the EMBL Workshop: From 3D Light to 3D Electron Microscopy.
Vancouver
1.
Roels J, Aelterman J, De Vylder J, Luong H, Lippens S, Guerin C, et al. From a biological sample to high-quality digital image representation: avoiding and restoring image artifacts through proper acquisition and image restoration. 2016.
MLA
Roels, Joris, Jan Aelterman, Jonas De Vylder, et al. “From a Biological Sample to High-quality Digital Image Representation: Avoiding and Restoring Image Artifacts Through Proper Acquisition and Image Restoration.” 2016. Print.
@inproceedings{7084714,
  abstract     = {The objective 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: sample preparation, image acquisition, image storage, restoration (if necessary) and analysis. In order to analyze the images and draw scientific conclusions, it is crucial to obtain image data that reflects reality as close as possible.  

We provide an overview of the fundamental artifacts and degradations that affect many micrographs during the acquisition and storage phase: non-uniform illumination, blur, noise, digitization and compression. Image restoration techniques such as flat-field correction, deconvolution and denoising can manipulate the acquired data in an effort to reduce the impact of artifacts due to physical and technical limitations, resulting in a better representation (i.e. a more accurate correspondence to reality) 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. For example, high compression rates may result into small file sizes, but typically introduce blocking artifacts that might affect consecutive image segmentation. 

It is essential to understand image acquisition and storage, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. 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 Luong, Hiep and Lippens, Saskia and Guerin, Chris and Saeys, Yvan and Philips, Wilfried},
  language     = {eng},
  location     = {Heidelberg, Germany},
  title        = {From a biological sample to high-quality digital image representation: avoiding and restoring image artifacts through proper acquisition and image restoration},
  year         = {2016},
}