Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.asoc.2017.11.039 http://hdl.handle.net/11449/179434 |
Resumo: | Histological images analysis is an important procedure to diagnose different types of cancer. One of them is the chronic lymphocytic leukemia (CLL), which can be identified by applying image segmentation techniques. This study presents an unsupervised method to segment neoplastic nuclei in CLL images. Firstly, deconvolution, histogram equalization and mean filter were applied to enhance nuclear regions. Then, a segmentation technique based on a combination of wavelet transform, fuzzy 2-partition entropy and genetic algorithm was used, followed by removal of false positive regions, and application of valley-emphasis and morphological operations. In order to evaluate the proposed algorithm H&E-stained histological images were used. In the accuracy metric, the proposed method attained more than 80%, which can surpass similar methods. This proposal presents spatial distribution that has a good consistency with a manual segmentation and lower overlapping rate than other techniques in the literature. |
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Repositório Institucional da UNESP |
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Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia imagesChronic lymphocytic leukemiaGenetic algorithmH&E-stained histological imagesNuclei segmentationWavelet transformHistological images analysis is an important procedure to diagnose different types of cancer. One of them is the chronic lymphocytic leukemia (CLL), which can be identified by applying image segmentation techniques. This study presents an unsupervised method to segment neoplastic nuclei in CLL images. Firstly, deconvolution, histogram equalization and mean filter were applied to enhance nuclear regions. Then, a segmentation technique based on a combination of wavelet transform, fuzzy 2-partition entropy and genetic algorithm was used, followed by removal of false positive regions, and application of valley-emphasis and morphological operations. In order to evaluate the proposed algorithm H&E-stained histological images were used. In the accuracy metric, the proposed method attained more than 80%, which can surpass similar methods. This proposal presents spatial distribution that has a good consistency with a manual segmentation and lower overlapping rate than other techniques in the literature.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Federal University of ABC Centre of Mathematics Computer Science and Cognition, Av. dos Estados, 5001Federal University of Uberlândia Department of Histology and Morphology Institute of Biomedical Science, Av. Amazonas, S/NSão Paulo State University (UNESP) Department of Computer Science and Statistics, R. Cristóvão Colombo 2265Federal University of Uberlândia Faculty of Computer Science, Av. João Naves de Ávila, 2121São Paulo State University (UNESP) Department of Computer Science and Statistics, R. Cristóvão Colombo 2265CAPES: 1575210FAPEMIG: TEC-APQ-02885-15Computer Science and CognitionUniversidade Federal de Uberlândia (UFU)Universidade Estadual Paulista (Unesp)Azevedo Tosta, Thaína A.Faria, Paulo RogérioBatista, Valério RamosNeves, Leandro Alves [UNESP]do Nascimento, Marcelo Zanchetta2018-12-11T17:35:10Z2018-12-11T17:35:10Z2018-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article49-58application/pdfhttp://dx.doi.org/10.1016/j.asoc.2017.11.039Applied Soft Computing Journal, v. 64, p. 49-58.1568-4946http://hdl.handle.net/11449/17943410.1016/j.asoc.2017.11.0392-s2.0-850379999452-s2.0-85037999945.pdf2139053814879312Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Soft Computing Journal1,199info:eu-repo/semantics/openAccess2024-01-15T06:17:54Zoai:repositorio.unesp.br:11449/179434Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:00:45.027727Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
title |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
spellingShingle |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images Azevedo Tosta, Thaína A. Chronic lymphocytic leukemia Genetic algorithm H&E-stained histological images Nuclei segmentation Wavelet transform |
title_short |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
title_full |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
title_fullStr |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
title_full_unstemmed |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
title_sort |
Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images |
author |
Azevedo Tosta, Thaína A. |
author_facet |
Azevedo Tosta, Thaína A. Faria, Paulo Rogério Batista, Valério Ramos Neves, Leandro Alves [UNESP] do Nascimento, Marcelo Zanchetta |
author_role |
author |
author2 |
Faria, Paulo Rogério Batista, Valério Ramos Neves, Leandro Alves [UNESP] do Nascimento, Marcelo Zanchetta |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Computer Science and Cognition Universidade Federal de Uberlândia (UFU) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Azevedo Tosta, Thaína A. Faria, Paulo Rogério Batista, Valério Ramos Neves, Leandro Alves [UNESP] do Nascimento, Marcelo Zanchetta |
dc.subject.por.fl_str_mv |
Chronic lymphocytic leukemia Genetic algorithm H&E-stained histological images Nuclei segmentation Wavelet transform |
topic |
Chronic lymphocytic leukemia Genetic algorithm H&E-stained histological images Nuclei segmentation Wavelet transform |
description |
Histological images analysis is an important procedure to diagnose different types of cancer. One of them is the chronic lymphocytic leukemia (CLL), which can be identified by applying image segmentation techniques. This study presents an unsupervised method to segment neoplastic nuclei in CLL images. Firstly, deconvolution, histogram equalization and mean filter were applied to enhance nuclear regions. Then, a segmentation technique based on a combination of wavelet transform, fuzzy 2-partition entropy and genetic algorithm was used, followed by removal of false positive regions, and application of valley-emphasis and morphological operations. In order to evaluate the proposed algorithm H&E-stained histological images were used. In the accuracy metric, the proposed method attained more than 80%, which can surpass similar methods. This proposal presents spatial distribution that has a good consistency with a manual segmentation and lower overlapping rate than other techniques in the literature. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:35:10Z 2018-12-11T17:35:10Z 2018-03-01 |
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 |
http://dx.doi.org/10.1016/j.asoc.2017.11.039 Applied Soft Computing Journal, v. 64, p. 49-58. 1568-4946 http://hdl.handle.net/11449/179434 10.1016/j.asoc.2017.11.039 2-s2.0-85037999945 2-s2.0-85037999945.pdf 2139053814879312 |
url |
http://dx.doi.org/10.1016/j.asoc.2017.11.039 http://hdl.handle.net/11449/179434 |
identifier_str_mv |
Applied Soft Computing Journal, v. 64, p. 49-58. 1568-4946 10.1016/j.asoc.2017.11.039 2-s2.0-85037999945 2-s2.0-85037999945.pdf 2139053814879312 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applied Soft Computing Journal 1,199 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
49-58 application/pdf |
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_ |
1808129481639985152 |