Unsupervised Bayesian classifier applied to the segmentation of retina image

C. Banga; F. Ghorbel; W. Pieczynski

doi:10.1109/IEMBS.1992.5762067

Unsupervised Bayesian classifier applied to the segmentation of retina image

C. Banga
, F. Ghorbel
, W. Pieczynski

CNRS SAMOVAR UMR 5157

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

In this paper, we use a stochastic model based on the finite normal mixture distribution identification for retina image segmentation. Local unsupervised methods blind and contextual, using the Expectation-Maximisation (EM) family algorithms for parameter estimation are tested. To get rid of the spatial dependence effect of pixels, a decorrelation processing is used before parameter estimation. The segmentation is then performed by Bayesian decision rule. Segmentation results are presented to prove the effectiveness of different approaches.

langue originale	Anglais
titre	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
rédacteurs en chef	Jean Louis Coatrieux, Jean Pierre Morucci, Swamy Laxminarayan, Robert Plonsey
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	1847-1848
Nombre de pages	2
ISBN (Electronique)	0780307852
Les DOIs	https://doi.org/10.1109/IEMBS.1992.5762067
état	Publié - 1 janv. 1992
Modification externe	Oui
Evénement	14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992 - Paris, France Durée: 29 oct. 1992 → 1 nov. 1992

Série de publications

Nom	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	5
ISSN (imprimé)	1557-170X

Une conférence

Une conférence	14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
Pays/Territoire	France
La ville	Paris
période	29/10/92 → 1/11/92

Accès au document

10.1109/IEMBS.1992.5762067

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Banga, C., Ghorbel, F., & Pieczynski, W. (1992). Unsupervised Bayesian classifier applied to the segmentation of retina image. Dans J. L. Coatrieux, J. P. Morucci, S. Laxminarayan, & R. Plonsey (eds.), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992 (p. 1847-1848). Article 5762067 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol 5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEMBS.1992.5762067

Banga, C. ; Ghorbel, F. ; Pieczynski, W. / Unsupervised Bayesian classifier applied to the segmentation of retina image. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992. Editeur / Jean Louis Coatrieux ; Jean Pierre Morucci ; Swamy Laxminarayan ; Robert Plonsey. Institute of Electrical and Electronics Engineers Inc., 1992. p. 1847-1848 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Unsupervised Bayesian classifier applied to the segmentation of retina image",

abstract = "In this paper, we use a stochastic model based on the finite normal mixture distribution identification for retina image segmentation. Local unsupervised methods blind and contextual, using the Expectation-Maximisation (EM) family algorithms for parameter estimation are tested. To get rid of the spatial dependence effect of pixels, a decorrelation processing is used before parameter estimation. The segmentation is then performed by Bayesian decision rule. Segmentation results are presented to prove the effectiveness of different approaches.",

author = "C. Banga and F. Ghorbel and W. Pieczynski",

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doi = "10.1109/IEMBS.1992.5762067",

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Banga, C, Ghorbel, F & Pieczynski, W 1992, Unsupervised Bayesian classifier applied to the segmentation of retina image. Dans JL Coatrieux, JP Morucci, S Laxminarayan & R Plonsey (eds), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992., 5762067, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, VOL. 5, Institute of Electrical and Electronics Engineers Inc., p. 1847-1848, 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992, Paris, France, 29/10/92. https://doi.org/10.1109/IEMBS.1992.5762067

Unsupervised Bayesian classifier applied to the segmentation of retina image. / Banga, C.; Ghorbel, F.; Pieczynski, W.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992. Ed. / Jean Louis Coatrieux; Jean Pierre Morucci; Swamy Laxminarayan; Robert Plonsey. Institute of Electrical and Electronics Engineers Inc., 1992. p. 1847-1848 5762067 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol 5).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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N2 - In this paper, we use a stochastic model based on the finite normal mixture distribution identification for retina image segmentation. Local unsupervised methods blind and contextual, using the Expectation-Maximisation (EM) family algorithms for parameter estimation are tested. To get rid of the spatial dependence effect of pixels, a decorrelation processing is used before parameter estimation. The segmentation is then performed by Bayesian decision rule. Segmentation results are presented to prove the effectiveness of different approaches.

AB - In this paper, we use a stochastic model based on the finite normal mixture distribution identification for retina image segmentation. Local unsupervised methods blind and contextual, using the Expectation-Maximisation (EM) family algorithms for parameter estimation are tested. To get rid of the spatial dependence effect of pixels, a decorrelation processing is used before parameter estimation. The segmentation is then performed by Bayesian decision rule. Segmentation results are presented to prove the effectiveness of different approaches.

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T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992

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Banga C, Ghorbel F, Pieczynski W. Unsupervised Bayesian classifier applied to the segmentation of retina image. Dans Coatrieux JL, Morucci JP, Laxminarayan S, Plonsey R, rédacteurs en chef, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992. Institute of Electrical and Electronics Engineers Inc. 1992. p. 1847-1848. 5762067. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/IEMBS.1992.5762067

Unsupervised Bayesian classifier applied to the segmentation of retina image

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