Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Shahrooz Faghih-Roohi"'
Autor:
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Matthan Caan, George Yiasemis, Lívia Rodrigues, Alexandre Lopes, Helio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita Nogovitsyn, Wallace Loos, Richard Frayne, Roberto Souza
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quali
Externí odkaz:
https://doaj.org/article/6261672881b846e09b8609e567b018fc
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214
MICCAI (5)
MICCAI (5)
In the medical field, semantic segmentation has recently been dominated by deep-learning based image processing methods. Convolutional Neural Network approaches analyze image patches, draw complex features and latent representations and take advantag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d70d4350bd6328cbc37e1daf35ca936c
https://doi.org/10.1007/978-3-030-59722-1_67
https://doi.org/10.1007/978-3-030-59722-1_67
Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI
Publikováno v:
Pattern Recognition. 63:667-679
In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low rank tensor plus sparse tensor decompositio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b9158ccecd5b12c0b09ecc9c4b89ea5
https://doi.org/10.1016/j.patcog.2016.09.040
https://doi.org/10.1016/j.patcog.2016.09.040
Publikováno v:
Pattern Recognition. 63:518-530
In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space. Unlike traditional dictionary learning, our method integrates both glob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4f2321531fa63102f393f4ddfac7425
https://doi.org/10.1016/j.patcog.2016.09.038
https://doi.org/10.1016/j.patcog.2016.09.038
Publikováno v:
ICIP
We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6dc242d9e4c57b8d474df415def2e0c0
https://doi.org/10.1109/icip.2016.7532450
https://doi.org/10.1109/icip.2016.7532450
Publikováno v:
ICIP
In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14f912fb3675ec01e5279a35989d7387
https://doi.org/10.1109/icip.2016.7532662
https://doi.org/10.1109/icip.2016.7532662
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery. 8:335-351
Statistical shape models have shown improved reliability and consistency in cardiac image segmentation. They incorporate a sufficient amount of a priori knowledge from the training datasets and solve some major problems such as noise and image artifa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d48d35a417dbddaf4ae6797d710859a6
https://doi.org/10.1007/s11548-012-0787-1
https://doi.org/10.1007/s11548-012-0787-1
Autor:
Shahrooz Faghih-Roohi, Hamid Soltanian-Zadeh, Seyyed Ehsan Mahmoudi, Ahmad Sabouri, Alireza Akhondi-Asl, Roohollah Rahmani, Vahid Taimouri
Publikováno v:
Computer Methods and Programs in Biomedicine. 98:172-182
There are many medical image processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebb0ba4375ace5c26410ebfc339d38fb
https://doi.org/10.1016/j.cmpb.2009.11.012
https://doi.org/10.1016/j.cmpb.2009.11.012
Publikováno v:
BIOIMAGING
It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-ran
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d69903c3e0f13d75492a3acfaf666856
Publikováno v:
ICARCV
Identifying the presence of Anti-Nuclear Antibody (ANA) in Human Epithelial Type-2 (HEp-2) cells via Indirect Immunofluoresence (IIF) images is commonly used to detect various diseases in clinical pathology tests. The main task at hand is the classif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ec9650f7189a68235f594ee384c816a