@InProceedings{ Ba2019.5,
author = {Bazzi, Farah and Mescam, Muriel and Basarab, Adrian and Kouam\'e, Denis},
title = "{On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging (regular paper)}",
booktitle = "{IEEE International Engineering in Medicine and Biology Conference (EMBC 2019), Berlin, 23/07/2019-27/07/2019}",
year = {2019},
month = {July},
publisher = {IEEE : Institute of Electrical and Electronics Engineers},
address = {http://www.ieee.org/},
pages = {(on line)},
language = {anglais},
URL = {https://doi.org/10.1109/EMBC.2019.8857959 - https://oatao.univ-toulouse.fr/26199/},
abstract = {The objective of this work is to apply 3D super resolution (SR) techniques to brain magnetic resonance (MR) image restoration. Two 3D SR methods are considered following different trends: one recently proposed
tensor-based approach and one inverse problem algorithm based on total variation and low rank regularization. The evaluation of their effectiveness is assessed through the segmentation of brain compartments: gray matter, white
matter and cerebrospinal fluid. The two algorithms are qualitatively and quantitatively evaluated on simulated images with ground truth available and on experimental data. The originality of this work is to consider the SR methods
as an initial step towards the final segmentation task. The results show the ability of both methods to overcome the loss of spatial resolution and to facilitate the segmentation of brain structures with improved accuracy compared
to native low-resolution MR images. Both algorithms achieved almost equivalent results with a highly reduced computational time cost for the tensor-based approach.}
}

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Two 3D SR methods are considered following different trends: one recently proposed tensor-based approach and one inverse problem algorithm based on total variation and low rank regularization. The evaluation of their effectiveness is assessed through the segmentation of brain compartments: gray matter, white matter and cerebrospinal fluid. The two algorithms are qualitatively and quantitatively evaluated on simulated images with ground truth available and on experimental data. The originality of this work is to consider the SR methods as an initial step towards the final segmentation task. The results show the ability of both methods to overcome the loss of spatial resolution and to facilitate the segmentation of brain structures with improved accuracy compared to native low-resolution MR images. Both algorithms achieved almost equivalent results with a highly reduced computational time cost for the tensor-based approach.","bibtex":"@InProceedings{ Ba2019.5,\nauthor = {Bazzi, Farah and Mescam, Muriel and Basarab, Adrian and Kouam\\'e, Denis},\ntitle = \"{On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging (regular paper)}\",\nbooktitle = \"{IEEE International Engineering in Medicine and Biology Conference (EMBC 2019), Berlin, 23/07/2019-27/07/2019}\",\nyear = {2019},\nmonth = {July},\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\naddress = {http://www.ieee.org/},\npages = {(on line)},\nlanguage = {anglais},\nURL = {https://doi.org/10.1109/EMBC.2019.8857959 - https://oatao.univ-toulouse.fr/26199/},\nabstract = {The objective of this work is to apply 3D super resolution (SR) techniques to brain magnetic resonance (MR) image restoration. Two 3D SR methods are considered following different trends: one recently proposed\ntensor-based approach and one inverse problem algorithm based on total variation and low rank regularization. The evaluation of their effectiveness is assessed through the segmentation of brain compartments: gray matter, white\nmatter and cerebrospinal fluid. The two algorithms are qualitatively and quantitatively evaluated on simulated images with ground truth available and on experimental data. The originality of this work is to consider the SR methods\nas an initial step towards the final segmentation task. The results show the ability of both methods to overcome the loss of spatial resolution and to facilitate the segmentation of brain structures with improved accuracy compared\nto native low-resolution MR images. 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