Detection of cancer in animal tissues: a wavelet approach
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/50848 |
Resumo: | Considering that the biospeckle laser is a dynamic interferometric phenomenon adopted as a tool to monitor changes in biological samples and that the temporal variation of speckle pattern depend on the activity level of the sample surface illuminated, this work proposes to analyse the time-varying scale-mixing matrix. Using two-dimensional scale-mixing wavelet transform several descriptive summaries varying on time are derived. These descriptors are signature of image regularity and fractality useful in tissue classification. In this work we propose to verify the behavior of the energy-flux between the scales, considering a set of 128 images to classifying cancer areas in images of an anaplastic mammary carcinoma in a female canine and in images of skin cancer in a cat obtained over time. The time-varying spectral slopes applied in the analysis of dissimilarities of tissues allowed to note that healthy area descriptors have lower values than cancer area descriptors, resulting in higher Hurst exponents. By using scaling properties of tissue images, we have captured information contained in the background tissue of images which is not utilized when only considering traditional morphological analysis. |
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Detection of cancer in animal tissues: a wavelet approachAnimal carcinoma tissuesHurst exponentImage analysisMultiscale analysisNon-decimated wavelet transformConsidering that the biospeckle laser is a dynamic interferometric phenomenon adopted as a tool to monitor changes in biological samples and that the temporal variation of speckle pattern depend on the activity level of the sample surface illuminated, this work proposes to analyse the time-varying scale-mixing matrix. Using two-dimensional scale-mixing wavelet transform several descriptive summaries varying on time are derived. These descriptors are signature of image regularity and fractality useful in tissue classification. In this work we propose to verify the behavior of the energy-flux between the scales, considering a set of 128 images to classifying cancer areas in images of an anaplastic mammary carcinoma in a female canine and in images of skin cancer in a cat obtained over time. The time-varying spectral slopes applied in the analysis of dissimilarities of tissues allowed to note that healthy area descriptors have lower values than cancer area descriptors, resulting in higher Hurst exponents. By using scaling properties of tissue images, we have captured information contained in the background tissue of images which is not utilized when only considering traditional morphological analysis.Universidade Federal de Lavras2022-08-04T22:28:28Z2022-08-04T22:28:28Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSÁFADI, T. Detection of cancer in animal tissues: a wavelet approach. Brazilian Journal of Biometrics, Lavras, v. 40, n. 1, p. 120-137, 2022. DOI: 10.28951/bjb.v40i1.557.http://repositorio.ufla.br/jspui/handle/1/50848Brazilian Journal of Biometricsreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSáfadi, Thelmaeng2023-05-19T18:51:12Zoai:localhost:1/50848Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-19T18:51:12Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Detection of cancer in animal tissues: a wavelet approach |
title |
Detection of cancer in animal tissues: a wavelet approach |
spellingShingle |
Detection of cancer in animal tissues: a wavelet approach Sáfadi, Thelma Animal carcinoma tissues Hurst exponent Image analysis Multiscale analysis Non-decimated wavelet transform |
title_short |
Detection of cancer in animal tissues: a wavelet approach |
title_full |
Detection of cancer in animal tissues: a wavelet approach |
title_fullStr |
Detection of cancer in animal tissues: a wavelet approach |
title_full_unstemmed |
Detection of cancer in animal tissues: a wavelet approach |
title_sort |
Detection of cancer in animal tissues: a wavelet approach |
author |
Sáfadi, Thelma |
author_facet |
Sáfadi, Thelma |
author_role |
author |
dc.contributor.author.fl_str_mv |
Sáfadi, Thelma |
dc.subject.por.fl_str_mv |
Animal carcinoma tissues Hurst exponent Image analysis Multiscale analysis Non-decimated wavelet transform |
topic |
Animal carcinoma tissues Hurst exponent Image analysis Multiscale analysis Non-decimated wavelet transform |
description |
Considering that the biospeckle laser is a dynamic interferometric phenomenon adopted as a tool to monitor changes in biological samples and that the temporal variation of speckle pattern depend on the activity level of the sample surface illuminated, this work proposes to analyse the time-varying scale-mixing matrix. Using two-dimensional scale-mixing wavelet transform several descriptive summaries varying on time are derived. These descriptors are signature of image regularity and fractality useful in tissue classification. In this work we propose to verify the behavior of the energy-flux between the scales, considering a set of 128 images to classifying cancer areas in images of an anaplastic mammary carcinoma in a female canine and in images of skin cancer in a cat obtained over time. The time-varying spectral slopes applied in the analysis of dissimilarities of tissues allowed to note that healthy area descriptors have lower values than cancer area descriptors, resulting in higher Hurst exponents. By using scaling properties of tissue images, we have captured information contained in the background tissue of images which is not utilized when only considering traditional morphological analysis. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-08-04T22:28:28Z 2022-08-04T22:28:28Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
SÁFADI, T. Detection of cancer in animal tissues: a wavelet approach. Brazilian Journal of Biometrics, Lavras, v. 40, n. 1, p. 120-137, 2022. DOI: 10.28951/bjb.v40i1.557. http://repositorio.ufla.br/jspui/handle/1/50848 |
identifier_str_mv |
SÁFADI, T. Detection of cancer in animal tissues: a wavelet approach. Brazilian Journal of Biometrics, Lavras, v. 40, n. 1, p. 120-137, 2022. DOI: 10.28951/bjb.v40i1.557. |
url |
http://repositorio.ufla.br/jspui/handle/1/50848 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Lavras |
publisher.none.fl_str_mv |
Universidade Federal de Lavras |
dc.source.none.fl_str_mv |
Brazilian Journal of Biometrics reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1815439266949365760 |