Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/BIBM49941.2020.9313575 http://hdl.handle.net/11449/207223 |
Resumo: | Fracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques. |
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Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological imagescolor imagecolorectal cancerfractal dimensionHiguchimultidimensionalFracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques.Sao Paulo State University (UNESP) Dep. of Computer Science and Statistics (DCCE)Federal University of Uberlândia (UFU) Faculty of Computation (FACOM)Federal Institute of Triângulo Mineiro (IFTM)Sao Paulo State University (UNESP) Dep. of Computer Science and Statistics (DCCE)Universidade Estadual Paulista (Unesp)Universidade Federal de Uberlândia (UFU)Federal Institute of Triângulo Mineiro (IFTM)Tenguam, Jaqueline Junko [UNESP]Rozendo, Guilherme Botazzo [UNESP]Roberto, Guilherme FreireNascimento, Marcelo Zanchetta DoMartins, Alessandro S.Neves, Leandro Alves [UNESP]2021-06-25T10:50:57Z2021-06-25T10:50:57Z2020-12-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2833-2839http://dx.doi.org/10.1109/BIBM49941.2020.9313575Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2833-2839.http://hdl.handle.net/11449/20722310.1109/BIBM49941.2020.93135752-s2.0-85100344160Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020info:eu-repo/semantics/openAccess2021-10-23T16:36:57Zoai:repositorio.unesp.br:11449/207223Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:31:32.280997Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
title |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
spellingShingle |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images Tenguam, Jaqueline Junko [UNESP] color image colorectal cancer fractal dimension Higuchi multidimensional |
title_short |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
title_full |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
title_fullStr |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
title_full_unstemmed |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
title_sort |
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images |
author |
Tenguam, Jaqueline Junko [UNESP] |
author_facet |
Tenguam, Jaqueline Junko [UNESP] Rozendo, Guilherme Botazzo [UNESP] Roberto, Guilherme Freire Nascimento, Marcelo Zanchetta Do Martins, Alessandro S. Neves, Leandro Alves [UNESP] |
author_role |
author |
author2 |
Rozendo, Guilherme Botazzo [UNESP] Roberto, Guilherme Freire Nascimento, Marcelo Zanchetta Do Martins, Alessandro S. Neves, Leandro Alves [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Uberlândia (UFU) Federal Institute of Triângulo Mineiro (IFTM) |
dc.contributor.author.fl_str_mv |
Tenguam, Jaqueline Junko [UNESP] Rozendo, Guilherme Botazzo [UNESP] Roberto, Guilherme Freire Nascimento, Marcelo Zanchetta Do Martins, Alessandro S. Neves, Leandro Alves [UNESP] |
dc.subject.por.fl_str_mv |
color image colorectal cancer fractal dimension Higuchi multidimensional |
topic |
color image colorectal cancer fractal dimension Higuchi multidimensional |
description |
Fracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-16 2021-06-25T10:50:57Z 2021-06-25T10:50:57Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/BIBM49941.2020.9313575 Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2833-2839. http://hdl.handle.net/11449/207223 10.1109/BIBM49941.2020.9313575 2-s2.0-85100344160 |
url |
http://dx.doi.org/10.1109/BIBM49941.2020.9313575 http://hdl.handle.net/11449/207223 |
identifier_str_mv |
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2833-2839. 10.1109/BIBM49941.2020.9313575 2-s2.0-85100344160 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2833-2839 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129330345148416 |