Improving the non-extensive medical image segmentation based on Tsallis entropy
Autor(a) principal: | |
---|---|
Data de Publicação: | 2011 |
Outros Autores: | |
Tipo de documento: | Artigo |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da FEI |
Texto Completo: | https://repositorio.fei.edu.br/handle/FEI/981 https://doi.org/10.1007/s10044-011-0225-y |
Resumo: | Thresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently, M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property, which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data. |
id |
FEI_6692dfc01a35da9318a72f4df2e72e83 |
---|---|
oai_identifier_str |
oai:repositorio.fei.edu.br:FEI/981 |
network_acronym_str |
FEI |
network_name_str |
Biblioteca Digital de Teses e Dissertações da FEI |
repository_id_str |
|
spelling |
RODRIGUES, PauloGIRALDI, G. A.2019-08-17T20:00:28Z2019-08-17T20:00:28Z2011RODRIGUES, Paulo; GIRALDI, G. A. Improving the non-extensive medical image segmentation based on Tsallis entropy. Pattern Analysis and Applications, v. 14, n. 4, p. 369-379, 2011.1433-7541https://repositorio.fei.edu.br/handle/FEI/98110.1007/s10044-011-0225-yhttps://doi.org/10.1007/s10044-011-0225-yThresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently, M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property, which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data.144369379Pattern Analysis and ApplicationsImproving the non-extensive medical image segmentation based on Tsallis entropyinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleNon-extensive entropyThresholding segmentationTsallis entropyinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da FEIinstname:Centro Universitário da Fundação Educacional Inaciana (FEI)instacron:FEIFEI/9812019-11-06 14:14:48.36Biblioteca Digital de Teses e Dissertaçõeshttp://sofia.fei.edu.br/pergamum/biblioteca/PRI |
dc.title.pt_BR.fl_str_mv |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
title |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
spellingShingle |
Improving the non-extensive medical image segmentation based on Tsallis entropy RODRIGUES, Paulo Non-extensive entropy Thresholding segmentation Tsallis entropy |
title_short |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
title_full |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
title_fullStr |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
title_full_unstemmed |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
title_sort |
Improving the non-extensive medical image segmentation based on Tsallis entropy |
author |
RODRIGUES, Paulo |
author_facet |
RODRIGUES, Paulo GIRALDI, G. A. |
author_role |
author |
author2 |
GIRALDI, G. A. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
RODRIGUES, Paulo GIRALDI, G. A. |
dc.subject.eng.fl_str_mv |
Non-extensive entropy Thresholding segmentation Tsallis entropy |
topic |
Non-extensive entropy Thresholding segmentation Tsallis entropy |
description |
Thresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently, M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property, which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data. |
publishDate |
2011 |
dc.date.issued.fl_str_mv |
2011 |
dc.date.accessioned.fl_str_mv |
2019-08-17T20:00:28Z |
dc.date.available.fl_str_mv |
2019-08-17T20:00:28Z |
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.citation.fl_str_mv |
RODRIGUES, Paulo; GIRALDI, G. A. Improving the non-extensive medical image segmentation based on Tsallis entropy. Pattern Analysis and Applications, v. 14, n. 4, p. 369-379, 2011. |
dc.identifier.uri.fl_str_mv |
https://repositorio.fei.edu.br/handle/FEI/981 |
dc.identifier.issn.none.fl_str_mv |
1433-7541 |
dc.identifier.doi.none.fl_str_mv |
10.1007/s10044-011-0225-y |
dc.identifier.url.none.fl_str_mv |
https://doi.org/10.1007/s10044-011-0225-y |
identifier_str_mv |
RODRIGUES, Paulo; GIRALDI, G. A. Improving the non-extensive medical image segmentation based on Tsallis entropy. Pattern Analysis and Applications, v. 14, n. 4, p. 369-379, 2011. 1433-7541 10.1007/s10044-011-0225-y |
url |
https://repositorio.fei.edu.br/handle/FEI/981 https://doi.org/10.1007/s10044-011-0225-y |
dc.relation.ispartof.none.fl_str_mv |
Pattern Analysis and Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da FEI instname:Centro Universitário da Fundação Educacional Inaciana (FEI) instacron:FEI |
instname_str |
Centro Universitário da Fundação Educacional Inaciana (FEI) |
instacron_str |
FEI |
institution |
FEI |
reponame_str |
Biblioteca Digital de Teses e Dissertações da FEI |
collection |
Biblioteca Digital de Teses e Dissertações da FEI |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1734750992807755776 |