A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment

Detalhes bibliográficos
Autor(a) principal: Hélder Filipe Oliveira
Data de Publicação: 2014
Outros Autores: Jaime Cardoso, Magalhães,A, Cardoso,MJ
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5936
http://dx.doi.org/10.1080/21681163.2013.858403
Resumo: Breast cancer conservative treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as the software tool Breast Cancer Conservative Treatment.cosmetic results (BCCT.core). Although results are satisfying, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system a low-cost tool, this work studies the incorporation of the Microsoft Kinect sensor in BCCT evaluations. The aim is to enable the automatic joint detection of prominent points, both on depth and RGB images. Afterwards, using those prominent points, it is possible to obtain two-dimensional and volumetric features. Finally, the aesthetic result is achieved using machine learning techniques converted automatically from the set of measures defined. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. In addition, comparing with previous research, the procedure for detecting prominent points was automated. © 2013 © 2013 Taylor & Francis.
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spelling A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatmentBreast cancer conservative treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as the software tool Breast Cancer Conservative Treatment.cosmetic results (BCCT.core). Although results are satisfying, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system a low-cost tool, this work studies the incorporation of the Microsoft Kinect sensor in BCCT evaluations. The aim is to enable the automatic joint detection of prominent points, both on depth and RGB images. Afterwards, using those prominent points, it is possible to obtain two-dimensional and volumetric features. Finally, the aesthetic result is achieved using machine learning techniques converted automatically from the set of measures defined. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. In addition, comparing with previous research, the procedure for detecting prominent points was automated. © 2013 © 2013 Taylor & Francis.2018-01-11T18:55:00Z2014-01-01T00:00:00Z2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5936http://dx.doi.org/10.1080/21681163.2013.858403engHélder Filipe OliveiraJaime CardosoMagalhães,ACardoso,MJinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:37Zoai:repositorio.inesctec.pt:123456789/5936Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:23.620455Repositório 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 A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
title A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
spellingShingle A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
Hélder Filipe Oliveira
title_short A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
title_full A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
title_fullStr A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
title_full_unstemmed A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
title_sort A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
author Hélder Filipe Oliveira
author_facet Hélder Filipe Oliveira
Jaime Cardoso
Magalhães,A
Cardoso,MJ
author_role author
author2 Jaime Cardoso
Magalhães,A
Cardoso,MJ
author2_role author
author
author
dc.contributor.author.fl_str_mv Hélder Filipe Oliveira
Jaime Cardoso
Magalhães,A
Cardoso,MJ
description Breast cancer conservative treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as the software tool Breast Cancer Conservative Treatment.cosmetic results (BCCT.core). Although results are satisfying, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system a low-cost tool, this work studies the incorporation of the Microsoft Kinect sensor in BCCT evaluations. The aim is to enable the automatic joint detection of prominent points, both on depth and RGB images. Afterwards, using those prominent points, it is possible to obtain two-dimensional and volumetric features. Finally, the aesthetic result is achieved using machine learning techniques converted automatically from the set of measures defined. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. In addition, comparing with previous research, the procedure for detecting prominent points was automated. © 2013 © 2013 Taylor & Francis.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01T00:00:00Z
2014
2018-01-11T18:55:00Z
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http://dx.doi.org/10.1080/21681163.2013.858403
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http://dx.doi.org/10.1080/21681163.2013.858403
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