Ulcer Segmentation and Tissue Classification using Color Texture Clustering
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Trabalho de conclusão de curso |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/34203 |
Resumo: | Chronic Wounds are ulcers presenting a difficult or nearly interrupted cicatrization process that increases the risk of complications to the health of patients, like amputations and infections. This research proposes a general noninvasive methodology for the segmentation and analysis of images of chronic wounds by computing the wound areas affected by necrosis, as opposed to invasive techniques that are commonly used for this calculation, such as manual planimetry with plastic films. We investigated algorithms to perform the segmentation of wounds and classification of tissues as Necrotic, Granulation or Slough. In the proposed methodology, we used histogram based textural descriptions, that were compared by using the Earth Mover's Distance, and proposed a color space reduction methodology that increased the reported accuracies, specificities, sensitivities and Dice coefficients. We also developed a mobile app prototype to show that it is possible to employ such application for supporting Larval Therapy on mobile devices. |
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Marques, Vítor de GodeiroBruno Santana da SilvaCanuto, Anne Magaly de PaulaSantos, Selan Rodrigues dosCarvalho, Bruno Motta de2018-12-07T19:42:11Z2021-09-20T11:47:02Z2018-12-07T19:42:11Z2021-09-20T11:47:02Z2018-11-2320180008316Marques, Vítor de Godeiro. Ulcer segmentation and tissue classification using color texture clustering. 85f. Monografia (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada Universidade Federal do Rio Grande do Norte. Natal, 2018.https://repositorio.ufrn.br/handle/123456789/34203Universidade Federal do Rio Grande do NorteUFRNBrasilCiência da ComputaçãoLarval therapyChronic woundsImage segmentationTissue classificationColor image analysisClusteringUlcer Segmentation and Tissue Classification using Color Texture Clusteringinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesisChronic Wounds are ulcers presenting a difficult or nearly interrupted cicatrization process that increases the risk of complications to the health of patients, like amputations and infections. This research proposes a general noninvasive methodology for the segmentation and analysis of images of chronic wounds by computing the wound areas affected by necrosis, as opposed to invasive techniques that are commonly used for this calculation, such as manual planimetry with plastic films. We investigated algorithms to perform the segmentation of wounds and classification of tissues as Necrotic, Granulation or Slough. In the proposed methodology, we used histogram based textural descriptions, that were compared by using the Earth Mover's Distance, and proposed a color space reduction methodology that increased the reported accuracies, specificities, sensitivities and Dice coefficients. We also developed a mobile app prototype to show that it is possible to employ such application for supporting Larval Therapy on mobile devices.engreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNinfo:eu-repo/semantics/openAccessORIGINALUlcerSegmentationClassification_Marques_2018.pdfapplication/pdf27362084https://repositorio.ufrn.br/bitstream/123456789/34203/1/UlcerSegmentationClassification_Marques_2018.pdfe799629e81c5de4fb83f3c75a4a88a22MD51LICENSElicense.txttext/plain1748https://repositorio.ufrn.br/bitstream/123456789/34203/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52CC-LICENSElicense_rdfapplication/octet-stream701https://repositorio.ufrn.br/bitstream/123456789/34203/3/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD53TEXTUlcerSegmentationClassification_marques_2018.pdf.txtExtracted texttext/plain124529https://repositorio.ufrn.br/bitstream/123456789/34203/4/UlcerSegmentationClassification_marques_2018.pdf.txt21b1e5dd98a7b212919ad5bb46fe2599MD54123456789/342032023-03-09 19:24:56.153oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2023-03-09T22:24:56Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
title |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
spellingShingle |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering Marques, Vítor de Godeiro Larval therapy Chronic wounds Image segmentation Tissue classification Color image analysis Clustering |
title_short |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
title_full |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
title_fullStr |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
title_full_unstemmed |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
title_sort |
Ulcer Segmentation and Tissue Classification using Color Texture Clustering |
author |
Marques, Vítor de Godeiro |
author_facet |
Marques, Vítor de Godeiro |
author_role |
author |
dc.contributor.referees1.none.fl_str_mv |
Canuto, Anne Magaly de Paula |
dc.contributor.referees2.none.fl_str_mv |
Santos, Selan Rodrigues dos |
dc.contributor.author.fl_str_mv |
Marques, Vítor de Godeiro |
dc.contributor.advisor-co1.fl_str_mv |
Bruno Santana da Silva |
dc.contributor.advisor1.fl_str_mv |
Carvalho, Bruno Motta de |
contributor_str_mv |
Bruno Santana da Silva Carvalho, Bruno Motta de |
dc.subject.por.fl_str_mv |
Larval therapy Chronic wounds Image segmentation Tissue classification Color image analysis Clustering |
topic |
Larval therapy Chronic wounds Image segmentation Tissue classification Color image analysis Clustering |
description |
Chronic Wounds are ulcers presenting a difficult or nearly interrupted cicatrization process that increases the risk of complications to the health of patients, like amputations and infections. This research proposes a general noninvasive methodology for the segmentation and analysis of images of chronic wounds by computing the wound areas affected by necrosis, as opposed to invasive techniques that are commonly used for this calculation, such as manual planimetry with plastic films. We investigated algorithms to perform the segmentation of wounds and classification of tissues as Necrotic, Granulation or Slough. In the proposed methodology, we used histogram based textural descriptions, that were compared by using the Earth Mover's Distance, and proposed a color space reduction methodology that increased the reported accuracies, specificities, sensitivities and Dice coefficients. We also developed a mobile app prototype to show that it is possible to employ such application for supporting Larval Therapy on mobile devices. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-12-07T19:42:11Z 2021-09-20T11:47:02Z |
dc.date.available.fl_str_mv |
2018-12-07T19:42:11Z 2021-09-20T11:47:02Z |
dc.date.issued.fl_str_mv |
2018-11-23 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
format |
bachelorThesis |
status_str |
publishedVersion |
dc.identifier.pt_BR.fl_str_mv |
20180008316 |
dc.identifier.citation.fl_str_mv |
Marques, Vítor de Godeiro. Ulcer segmentation and tissue classification using color texture clustering. 85f. Monografia (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada Universidade Federal do Rio Grande do Norte. Natal, 2018. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/34203 |
identifier_str_mv |
20180008316 Marques, Vítor de Godeiro. Ulcer segmentation and tissue classification using color texture clustering. 85f. Monografia (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada Universidade Federal do Rio Grande do Norte. Natal, 2018. |
url |
https://repositorio.ufrn.br/handle/123456789/34203 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio Grande do Norte |
dc.publisher.initials.fl_str_mv |
UFRN |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Ciência da Computação |
publisher.none.fl_str_mv |
Universidade Federal do Rio Grande do Norte |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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UFRN |
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UFRN |
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Repositório Institucional da UFRN |
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Repositório Institucional da UFRN |
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