Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/52816 |
Resumo: | Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis. |
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Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy imagesBladder tumorCystoscopyDiscrete wavelet transformMultilayer perceptronSegmentationScience & TechnologyCorrect classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.This work is supported by FCT under Project No. UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI) under Project No. POCI-01-0145-FEDER-006941.info:eu-repo/semantics/publishedVersionIOP PublishingUniversidade do MinhoFreitas, Nuno R.Vieira, Pedro MiguelLima, Estêvão Augusto Rodrigues deLima, C. S.20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/52816engFreitas, N. R., Vieira, P. M., Lima, E., & Lima, C. S. (2018, February 2). Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images. Physics in Medicine & Biology. IOP Publishing. http://doi.org/10.1088/1361-6560/aaa3af0031-91551361-656010.1088/1361-6560/aaa3af29271350info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:42:03Zoai:repositorium.sdum.uminho.pt:1822/52816Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:39:12.578839Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
title |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
spellingShingle |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images Freitas, Nuno R. Bladder tumor Cystoscopy Discrete wavelet transform Multilayer perceptron Segmentation Science & Technology |
title_short |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
title_full |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
title_fullStr |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
title_full_unstemmed |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
title_sort |
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images |
author |
Freitas, Nuno R. |
author_facet |
Freitas, Nuno R. Vieira, Pedro Miguel Lima, Estêvão Augusto Rodrigues de Lima, C. S. |
author_role |
author |
author2 |
Vieira, Pedro Miguel Lima, Estêvão Augusto Rodrigues de Lima, C. S. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Freitas, Nuno R. Vieira, Pedro Miguel Lima, Estêvão Augusto Rodrigues de Lima, C. S. |
dc.subject.por.fl_str_mv |
Bladder tumor Cystoscopy Discrete wavelet transform Multilayer perceptron Segmentation Science & Technology |
topic |
Bladder tumor Cystoscopy Discrete wavelet transform Multilayer perceptron Segmentation Science & Technology |
description |
Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z |
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 |
https://hdl.handle.net/1822/52816 |
url |
https://hdl.handle.net/1822/52816 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Freitas, N. R., Vieira, P. M., Lima, E., & Lima, C. S. (2018, February 2). Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images. Physics in Medicine & Biology. IOP Publishing. http://doi.org/10.1088/1361-6560/aaa3af 0031-9155 1361-6560 10.1088/1361-6560/aaa3af 29271350 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IOP Publishing |
publisher.none.fl_str_mv |
IOP Publishing |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132932188667904 |