Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic
Main Author: | |
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Publication Date: | 2015 |
Other Authors: | , , , , |
Format: | Conference object |
Language: | eng |
Source: | Repositório Institucional da UNESP |
Download full: | http://iopscience.iop.org/article/10.1088/1742-6596/574/1/012135/meta http://hdl.handle.net/11449/128818 |
Summary: | In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists. |
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Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logicIn this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.Universidade Federal de Uberlândia, Faculdade de Ciência da ComputaçãoHospital de Base de São José do Rio Preto, Departamento de PatologiaUniversidade Estadual Paulista, Departamento de Ciência da Computação e Estatística, Instituto de Biociências, Letras e Ciências Exatas de São José do Rio PretoIop Publishing LtdUniversidade Estadual Paulista (Unesp)Universidade Federal de Uberlândia (UFU)Faculdade de Medicina de São José do Rio Preto(FAMERP)Núcleo Transdisciplinar para Estudo do Caos e da Complexidade (NUTECC)Hospital de Base de São José do Rio PretoPavarino, Eduardo [UNESP]Neves, Leandro Alves [UNESP]Nascimento, Marcelo Zanchetta do [UNESP]Godoy, Moacir Fernandes deArruda, Pedro Francisco deSanti Neto, Dalísio de2015-10-21T13:13:59Z2015-10-21T13:13:59Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1-4application/pdfhttp://iopscience.iop.org/article/10.1088/1742-6596/574/1/012135/meta3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014). Bristol: Iop Publishing Ltd, v. 574, p. 1-4, 2015.1742-6588http://hdl.handle.net/11449/12881810.1088/1742-6596/574/1/012135WOS:000352595600135WOS000352595600135.pdf2139053814879312Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014)0,241info:eu-repo/semantics/openAccess2023-10-29T06:04:40Zoai:repositorio.unesp.br:11449/128818Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-10-29T06:04:40Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
title |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
spellingShingle |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic Pavarino, Eduardo [UNESP] |
title_short |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
title_full |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
title_fullStr |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
title_full_unstemmed |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
title_sort |
Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic |
author |
Pavarino, Eduardo [UNESP] |
author_facet |
Pavarino, Eduardo [UNESP] Neves, Leandro Alves [UNESP] Nascimento, Marcelo Zanchetta do [UNESP] Godoy, Moacir Fernandes de Arruda, Pedro Francisco de Santi Neto, Dalísio de |
author_role |
author |
author2 |
Neves, Leandro Alves [UNESP] Nascimento, Marcelo Zanchetta do [UNESP] Godoy, Moacir Fernandes de Arruda, Pedro Francisco de Santi Neto, Dalísio de |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Uberlândia (UFU) Faculdade de Medicina de São José do Rio Preto(FAMERP) Núcleo Transdisciplinar para Estudo do Caos e da Complexidade (NUTECC) Hospital de Base de São José do Rio Preto |
dc.contributor.author.fl_str_mv |
Pavarino, Eduardo [UNESP] Neves, Leandro Alves [UNESP] Nascimento, Marcelo Zanchetta do [UNESP] Godoy, Moacir Fernandes de Arruda, Pedro Francisco de Santi Neto, Dalísio de |
description |
In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-10-21T13:13:59Z 2015-10-21T13:13:59Z 2015-01-01 |
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://iopscience.iop.org/article/10.1088/1742-6596/574/1/012135/meta 3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014). Bristol: Iop Publishing Ltd, v. 574, p. 1-4, 2015. 1742-6588 http://hdl.handle.net/11449/128818 10.1088/1742-6596/574/1/012135 WOS:000352595600135 WOS000352595600135.pdf 2139053814879312 |
url |
http://iopscience.iop.org/article/10.1088/1742-6596/574/1/012135/meta http://hdl.handle.net/11449/128818 |
identifier_str_mv |
3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014). Bristol: Iop Publishing Ltd, v. 574, p. 1-4, 2015. 1742-6588 10.1088/1742-6596/574/1/012135 WOS:000352595600135 WOS000352595600135.pdf 2139053814879312 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014) 0,241 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-4 application/pdf |
dc.publisher.none.fl_str_mv |
Iop Publishing Ltd |
publisher.none.fl_str_mv |
Iop Publishing Ltd |
dc.source.none.fl_str_mv |
Web of Science 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 |
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