Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Hedi Yazid"'
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 7, Iss 6 (2014)
Case retrieval constitutes an interesting area of research which contributes to the evolution of several domains. The similarity measure module is a fundamental step in the retrieval process which affects remarkably on a retrieval system. In this con
Externí odkaz:
https://doaj.org/article/e26ed347f675485e82e95b8b68b35d1d
Publikováno v:
International Journal of Imaging Systems and Technology. 31:302-312
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::38ae60bb927864d97dbd2500a9c686f6
https://doi.org/10.1002/ima.22478
https://doi.org/10.1002/ima.22478
Publikováno v:
ATSIP
Image registration is a crucial task in medical applications and is perceived as an optimization problem which has an important interest in clinical diagnosis. In this work, we propose an optimization strategy based on a specific design of genetic al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fcc74cddc9b919723e2e4610c7264670
https://doi.org/10.1109/atsip49331.2020.9231906
https://doi.org/10.1109/atsip49331.2020.9231906
Autor:
Sourour Mesbahi, Hedi Yazid
Publikováno v:
ATSIP
This paper presents a neural network architecture for segmentation of medical images. We have chosen to test and implement various Convolutional Neural Network (CNN). We chose to apply this work on a topic of cerebral images segmentation containing b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc7f30a1859fb84110698deb23ee911b
https://doi.org/10.1109/atsip49331.2020.9231669
https://doi.org/10.1109/atsip49331.2020.9231669
Publikováno v:
SSD
Atlas-based segmentation is a high-level technique which provides highly accurate results particularly in the anatomical segmentation of medical images. The main idea of this technique consists in using a dataset of atlases which is perceived as a pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24ac450b78c151e79354a4ac38536ba9
https://doi.org/10.1109/ssd49366.2020.9364095
https://doi.org/10.1109/ssd49366.2020.9364095
Publikováno v:
2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).
Image registration is an important preprocessing step in medical imaging applications. It can be formulated as an optimization problem where the associated energy to be optimized is a non-convex function that often shows local optima. Unlike classica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12401ef9456a075236e2d6c2bf95eb25
https://doi.org/10.1109/setit.2016.7939932
https://doi.org/10.1109/setit.2016.7939932
Publikováno v:
SSD
This paper proposes a comparison between the Bayesian networks and the Possibilistic networks facing the treatment of a similarity measurement problem. The proposed similarity measure is incorporated in brain tumors MRI cases retrieval contribution.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::885c6d4ada66609953a9a0d9093b8246
https://doi.org/10.1109/ssd.2013.6564017
https://doi.org/10.1109/ssd.2013.6564017
Publikováno v:
CIMI
We propose in this paper a bayesian network based similarity measure for the retrieving of magnetic resonance imaging exams containing cerebral tumors. Bayesian networks proved their efficiency and reliability in several Artificial Intelligence probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a63793def13a1ea8949df890c469f149
https://doi.org/10.1109/cimi.2011.5952042
https://doi.org/10.1109/cimi.2011.5952042
Publikováno v:
ISSPIT
We propose in this paper a Bayesian model for the retrieving of MRI (magnetic resonance imaging) exams that contain cerebral tumors. Bayesian network proved its efficiency and reliability in several AI (Artificial Intelligence) problems and especiall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40f517b70fa0c133e3be506aa9873e72
https://doi.org/10.1109/isspit.2010.5711742
https://doi.org/10.1109/isspit.2010.5711742