Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-33904-3_24 http://hdl.handle.net/11449/198207 |
Resumo: | Lymphomas are neoplasms that originate in the lymphatic system and represent one of the most common types of cancer found in the World population. The feature analysis may contribute toward results of higher relevance in the classification of the lesions. Feature extraction methods are employed to obtain data that can indicate lymphoma incidence. In this work, we investigated the multiscale and multidimensional fractal geometry with colour channels and colour models for classification of lymphoma tissue images. The fractal features were extracted from the RGB and LAB models and colour channels. The fractal features were concatenated to form the feature vector. Finally, we employed the Hermite polynomial classifier in order to evaluate the performance of the proposed approach. The colour channels obtained of histological images achieved higher accuracy values, the obtained rates were between 94% and 97%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the lesion in lymphoma images. |
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Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma ImagesClassificationColour fractalHermite polynomialLymphomaLymphomas are neoplasms that originate in the lymphatic system and represent one of the most common types of cancer found in the World population. The feature analysis may contribute toward results of higher relevance in the classification of the lesions. Feature extraction methods are employed to obtain data that can indicate lymphoma incidence. In this work, we investigated the multiscale and multidimensional fractal geometry with colour channels and colour models for classification of lymphoma tissue images. The fractal features were extracted from the RGB and LAB models and colour channels. The fractal features were concatenated to form the feature vector. Finally, we employed the Hermite polynomial classifier in order to evaluate the performance of the proposed approach. The colour channels obtained of histological images achieved higher accuracy values, the obtained rates were between 94% and 97%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the lesion in lymphoma images.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Federal Institute of Triângulo MineiroDepartment of Computer Science and Statistics (UNESP) São Paulo State UniversityCenter of Mathematics Computing and Cognition Federal University of ABCDepartment of Histology and Morphology Institute of Biomedical Science Federal University of UberlândiaFaculty of Computer Science Federal University of UberlândiaDepartment of Computer Science and Statistics (UNESP) São Paulo State UniversityCNPq: 304848/2018-2CNPq: 313365/2018-0CNPq: 427114/2016-0CNPq: 430965/2018-4FAPEMIG: APQ-00578-18Federal Institute of Triângulo MineiroUniversidade Estadual Paulista (Unesp)Federal University of ABCUniversidade Federal de Uberlândia (UFU)Martins, Alessandro S.Neves, Leandro A. [UNESP]Faria, Paulo R.Tosta, Thaína A. A.Bruno, Daniel O. T.Longo, Leonardo C. [UNESP]do Nascimento, Marcelo Zanchetta2020-12-12T01:06:29Z2020-12-12T01:06:29Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject262-271http://dx.doi.org/10.1007/978-3-030-33904-3_24Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 262-271.1611-33490302-9743http://hdl.handle.net/11449/19820710.1007/978-3-030-33904-3_242-s2.0-85075698178Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2021-10-23T09:55:36Zoai:repositorio.unesp.br:11449/198207Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T09:55:36Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
title |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
spellingShingle |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images Martins, Alessandro S. Classification Colour fractal Hermite polynomial Lymphoma |
title_short |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
title_full |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
title_fullStr |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
title_full_unstemmed |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
title_sort |
Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images |
author |
Martins, Alessandro S. |
author_facet |
Martins, Alessandro S. Neves, Leandro A. [UNESP] Faria, Paulo R. Tosta, Thaína A. A. Bruno, Daniel O. T. Longo, Leonardo C. [UNESP] do Nascimento, Marcelo Zanchetta |
author_role |
author |
author2 |
Neves, Leandro A. [UNESP] Faria, Paulo R. Tosta, Thaína A. A. Bruno, Daniel O. T. Longo, Leonardo C. [UNESP] do Nascimento, Marcelo Zanchetta |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Federal Institute of Triângulo Mineiro Universidade Estadual Paulista (Unesp) Federal University of ABC Universidade Federal de Uberlândia (UFU) |
dc.contributor.author.fl_str_mv |
Martins, Alessandro S. Neves, Leandro A. [UNESP] Faria, Paulo R. Tosta, Thaína A. A. Bruno, Daniel O. T. Longo, Leonardo C. [UNESP] do Nascimento, Marcelo Zanchetta |
dc.subject.por.fl_str_mv |
Classification Colour fractal Hermite polynomial Lymphoma |
topic |
Classification Colour fractal Hermite polynomial Lymphoma |
description |
Lymphomas are neoplasms that originate in the lymphatic system and represent one of the most common types of cancer found in the World population. The feature analysis may contribute toward results of higher relevance in the classification of the lesions. Feature extraction methods are employed to obtain data that can indicate lymphoma incidence. In this work, we investigated the multiscale and multidimensional fractal geometry with colour channels and colour models for classification of lymphoma tissue images. The fractal features were extracted from the RGB and LAB models and colour channels. The fractal features were concatenated to form the feature vector. Finally, we employed the Hermite polynomial classifier in order to evaluate the performance of the proposed approach. The colour channels obtained of histological images achieved higher accuracy values, the obtained rates were between 94% and 97%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the lesion in lymphoma images. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T01:06:29Z 2020-12-12T01:06:29Z |
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/10.1007/978-3-030-33904-3_24 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 262-271. 1611-3349 0302-9743 http://hdl.handle.net/11449/198207 10.1007/978-3-030-33904-3_24 2-s2.0-85075698178 |
url |
http://dx.doi.org/10.1007/978-3-030-33904-3_24 http://hdl.handle.net/11449/198207 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 262-271. 1611-3349 0302-9743 10.1007/978-3-030-33904-3_24 2-s2.0-85075698178 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
262-271 |
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_ |
1799965519783133184 |