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Automatic resin duct detection and measurement from wood core images using convolutional neural networks
The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is ted...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154293/ https://www.ncbi.nlm.nih.gov/pubmed/37130881 http://dx.doi.org/10.1038/s41598-023-34304-7 |
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author | Fabijańska, Anna Cahalan, Gabriel D. |
author_facet | Fabijańska, Anna Cahalan, Gabriel D. |
author_sort | Fabijańska, Anna |
collection | PubMed |
description | The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively. |
format | Online Article Text |
id | pubmed-10154293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101542932023-05-04 Automatic resin duct detection and measurement from wood core images using convolutional neural networks Fabijańska, Anna Cahalan, Gabriel D. Sci Rep Article The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively. Nature Publishing Group UK 2023-05-02 /pmc/articles/PMC10154293/ /pubmed/37130881 http://dx.doi.org/10.1038/s41598-023-34304-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Fabijańska, Anna Cahalan, Gabriel D. Automatic resin duct detection and measurement from wood core images using convolutional neural networks |
title | Automatic resin duct detection and measurement from wood core images using convolutional neural networks |
title_full | Automatic resin duct detection and measurement from wood core images using convolutional neural networks |
title_fullStr | Automatic resin duct detection and measurement from wood core images using convolutional neural networks |
title_full_unstemmed | Automatic resin duct detection and measurement from wood core images using convolutional neural networks |
title_short | Automatic resin duct detection and measurement from wood core images using convolutional neural networks |
title_sort | automatic resin duct detection and measurement from wood core images using convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154293/ https://www.ncbi.nlm.nih.gov/pubmed/37130881 http://dx.doi.org/10.1038/s41598-023-34304-7 |
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