Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors
Autor(a) principal: | |
---|---|
Data de Publicação: | 2010 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/17771 |
Resumo: | This paper presents an approach to the automatic detection of small bowel tumors by processing endoscopic capsule images. The most significant texture information is selected by using wavelet processing and captured in the image domain from an appropriate synthesized image. Co-occurrence matrices are used to derive texture descriptors by modeling second order statistics of color image levels. These descriptors are then modeled by using third and fourth order moments in order to cope with distributions that tend to become non-Gaussian especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data and shows that higher order moments can be effective in modeling capsule endoscopic images regarding tumor detection. |
id |
RCAP_02ee68fcaf77434b84a5c21fb63eb627 |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/17771 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptorsNon-gaussianityHigher order momentsSmall bowel tumor detectionTexture descriptorsBio-medical imageWavelet processingThis paper presents an approach to the automatic detection of small bowel tumors by processing endoscopic capsule images. The most significant texture information is selected by using wavelet processing and captured in the image domain from an appropriate synthesized image. Co-occurrence matrices are used to derive texture descriptors by modeling second order statistics of color image levels. These descriptors are then modeled by using third and fourth order moments in order to cope with distributions that tend to become non-Gaussian especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data and shows that higher order moments can be effective in modeling capsule endoscopic images regarding tumor detection.Centre AlgoritmiUniversidade do MinhoBarbosa, DanielRamos, JaimeTavares, AdrianoLima, C. S.2010-09-102010-09-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/17771eng0973-7294http://www.ceserp.com/cp-jour/index.php?journal=ijts&page=issue&op=view&path%5B%5D=11info: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:RCAAP2023-07-21T12:33:47Zoai:repositorium.sdum.uminho.pt:1822/17771Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:29:21.310446Repositório 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 |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
title |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
spellingShingle |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors Barbosa, Daniel Non-gaussianity Higher order moments Small bowel tumor detection Texture descriptors Bio-medical image Wavelet processing |
title_short |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
title_full |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
title_fullStr |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
title_full_unstemmed |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
title_sort |
Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors |
author |
Barbosa, Daniel |
author_facet |
Barbosa, Daniel Ramos, Jaime Tavares, Adriano Lima, C. S. |
author_role |
author |
author2 |
Ramos, Jaime Tavares, Adriano Lima, C. S. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Barbosa, Daniel Ramos, Jaime Tavares, Adriano Lima, C. S. |
dc.subject.por.fl_str_mv |
Non-gaussianity Higher order moments Small bowel tumor detection Texture descriptors Bio-medical image Wavelet processing |
topic |
Non-gaussianity Higher order moments Small bowel tumor detection Texture descriptors Bio-medical image Wavelet processing |
description |
This paper presents an approach to the automatic detection of small bowel tumors by processing endoscopic capsule images. The most significant texture information is selected by using wavelet processing and captured in the image domain from an appropriate synthesized image. Co-occurrence matrices are used to derive texture descriptors by modeling second order statistics of color image levels. These descriptors are then modeled by using third and fourth order moments in order to cope with distributions that tend to become non-Gaussian especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data and shows that higher order moments can be effective in modeling capsule endoscopic images regarding tumor detection. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-09-10 2010-09-10T00:00:00Z |
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 |
http://hdl.handle.net/1822/17771 |
url |
http://hdl.handle.net/1822/17771 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0973-7294 http://www.ceserp.com/cp-jour/index.php?journal=ijts&page=issue&op=view&path%5B%5D=11 |
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.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 |
|
_version_ |
1799132792989155328 |