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Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform

This paper describes a new methodology for lane detection in Thin-Layer Chromatography images. An approach based on the continuous wavelet transform is used to enhance the relevant lane information contained in the intensity profile obtained from image data projection. Lane detection proceeds in thr...

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Detalles Bibliográficos
Autores principales: Moreira, Bruno, Sousa, António, Mendonça, Ana Maria, Campilho, Aurélio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792535/
https://www.ncbi.nlm.nih.gov/pubmed/24171044
http://dx.doi.org/10.1155/2013/218415
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author Moreira, Bruno
Sousa, António
Mendonça, Ana Maria
Campilho, Aurélio
author_facet Moreira, Bruno
Sousa, António
Mendonça, Ana Maria
Campilho, Aurélio
author_sort Moreira, Bruno
collection PubMed
description This paper describes a new methodology for lane detection in Thin-Layer Chromatography images. An approach based on the continuous wavelet transform is used to enhance the relevant lane information contained in the intensity profile obtained from image data projection. Lane detection proceeds in three phases: the first obtains a set of candidate lanes, which are validated or removed in the second phase; in the third phase, lane limits are calculated, and subtle lanes are recovered. The superior performance of the new solution was confirmed by a comparison with three other methodologies previously described in the literature.
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spelling pubmed-37925352013-10-29 Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform Moreira, Bruno Sousa, António Mendonça, Ana Maria Campilho, Aurélio Comput Math Methods Med Research Article This paper describes a new methodology for lane detection in Thin-Layer Chromatography images. An approach based on the continuous wavelet transform is used to enhance the relevant lane information contained in the intensity profile obtained from image data projection. Lane detection proceeds in three phases: the first obtains a set of candidate lanes, which are validated or removed in the second phase; in the third phase, lane limits are calculated, and subtle lanes are recovered. The superior performance of the new solution was confirmed by a comparison with three other methodologies previously described in the literature. Hindawi Publishing Corporation 2013 2013-09-19 /pmc/articles/PMC3792535/ /pubmed/24171044 http://dx.doi.org/10.1155/2013/218415 Text en Copyright © 2013 Bruno Moreira et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Moreira, Bruno
Sousa, António
Mendonça, Ana Maria
Campilho, Aurélio
Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform
title Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform
title_full Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform
title_fullStr Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform
title_full_unstemmed Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform
title_short Automatic Lane Segmentation in TLC Images Using the Continuous Wavelet Transform
title_sort automatic lane segmentation in tlc images using the continuous wavelet transform
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792535/
https://www.ncbi.nlm.nih.gov/pubmed/24171044
http://dx.doi.org/10.1155/2013/218415
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