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An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results....

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Detalles Bibliográficos
Autores principales: Berry, Jeffrey C., Fahlgren, Noah, Pokorny, Alexandria A., Bart, Rebecca S., Veley, Kira M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174877/
https://www.ncbi.nlm.nih.gov/pubmed/30310752
http://dx.doi.org/10.7717/peerj.5727
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author Berry, Jeffrey C.
Fahlgren, Noah
Pokorny, Alexandria A.
Bart, Rebecca S.
Veley, Kira M.
author_facet Berry, Jeffrey C.
Fahlgren, Noah
Pokorny, Alexandria A.
Bart, Rebecca S.
Veley, Kira M.
author_sort Berry, Jeffrey C.
collection PubMed
description High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
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spelling pubmed-61748772018-10-11 An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping Berry, Jeffrey C. Fahlgren, Noah Pokorny, Alexandria A. Bart, Rebecca S. Veley, Kira M. PeerJ Bioinformatics High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV. PeerJ Inc. 2018-10-04 /pmc/articles/PMC6174877/ /pubmed/30310752 http://dx.doi.org/10.7717/peerj.5727 Text en ©2018 Berry et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Berry, Jeffrey C.
Fahlgren, Noah
Pokorny, Alexandria A.
Bart, Rebecca S.
Veley, Kira M.
An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
title An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
title_full An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
title_fullStr An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
title_full_unstemmed An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
title_short An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
title_sort automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174877/
https://www.ncbi.nlm.nih.gov/pubmed/30310752
http://dx.doi.org/10.7717/peerj.5727
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