Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

Detalhes bibliográficos
Autor(a) principal: Freitas, Nuno R.
Data de Publicação: 2018
Outros Autores: Vieira, Pedro Miguel, Lima, Estêvão Augusto Rodrigues de, Lima, C. S.
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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spelling 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
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publisher.none.fl_str_mv IOP Publishing
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