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Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images
Brightest path tracing is a widely used image processing technique in several fields including biology, geography, and geology. However, despite the availability of many image processing libraries in Python, few offer an out-of-the-box implementation of a brightest path tracing algorithm. This paper...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370081/ https://www.ncbi.nlm.nih.gov/pubmed/37503184 http://dx.doi.org/10.1101/2023.07.16.549233 |
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author | Jha, Vasudha Cudmore, Robert H. |
author_facet | Jha, Vasudha Cudmore, Robert H. |
author_sort | Jha, Vasudha |
collection | PubMed |
description | Brightest path tracing is a widely used image processing technique in several fields including biology, geography, and geology. However, despite the availability of many image processing libraries in Python, few offer an out-of-the-box implementation of a brightest path tracing algorithm. This paper presents a Python package, brightest-path-lib, that efficiently finds the path with maximum brightness between points in a 2D or 3D image. An example graphical user interface is provided as a Napari plugin. Taken together, the package and plugin provide a powerful and extensible tool for users to efficiently trace structures of interest in 2D or 3D images, regardless of the type of structure being analyzed. |
format | Online Article Text |
id | pubmed-10370081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-103700812023-07-27 Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images Jha, Vasudha Cudmore, Robert H. bioRxiv Article Brightest path tracing is a widely used image processing technique in several fields including biology, geography, and geology. However, despite the availability of many image processing libraries in Python, few offer an out-of-the-box implementation of a brightest path tracing algorithm. This paper presents a Python package, brightest-path-lib, that efficiently finds the path with maximum brightness between points in a 2D or 3D image. An example graphical user interface is provided as a Napari plugin. Taken together, the package and plugin provide a powerful and extensible tool for users to efficiently trace structures of interest in 2D or 3D images, regardless of the type of structure being analyzed. Cold Spring Harbor Laboratory 2023-07-18 /pmc/articles/PMC10370081/ /pubmed/37503184 http://dx.doi.org/10.1101/2023.07.16.549233 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Jha, Vasudha Cudmore, Robert H. Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images |
title | Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images |
title_full | Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images |
title_fullStr | Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images |
title_full_unstemmed | Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images |
title_short | Brightest path tracing: A Python package to trace the brightest path in 2D and 3D images |
title_sort | brightest path tracing: a python package to trace the brightest path in 2d and 3d images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370081/ https://www.ncbi.nlm.nih.gov/pubmed/37503184 http://dx.doi.org/10.1101/2023.07.16.549233 |
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