Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator

Abderrazak Chahid; Hacene Serrai; Eric Achten; Taous Meriem Laleg-Kirati

doi:10.1109/BIOCAS.2017.8325107

Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator

Abderrazak Chahid, Hacene Serrai, Eric Achten, Taous Meriem Laleg-Kirati^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrödinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.

Original language	English (US)
Title of host publication	2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781509058037
DOIs	https://doi.org/10.1109/BIOCAS.2017.8325107
State	Published - Jul 2 2017
Event	2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Torino, Italy Duration: Oct 19 2017 → Oct 21 2017

Publication series

Name	2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
Volume	2018-January

Conference

Conference	2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017
Country/Territory	Italy
City	Torino
Period	10/19/17 → 10/21/17

Keywords

Magnetic Resonance Imaging (MRI)
Semi-Classical Signal Analysis (SCSA)
adaptive image denoising
eigenfunctions of the Schrodinger operator

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering
Biomedical Engineering

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10.1109/BIOCAS.2017.8325107

Cite this

Chahid, A., Serrai, H., Achten, E., & Laleg-Kirati, T. M. (2017). Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator. In 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings (pp. 1-4). (2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2017.8325107

Chahid, Abderrazak ; Serrai, Hacene ; Achten, Eric et al. / Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator. 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-4 (2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings).

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title = "Adaptive method for MRI enhancement using squared eigenfunctions of the Schr{\"o}dinger operator",

abstract = "Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schr{\"o}dinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.",

keywords = "Magnetic Resonance Imaging (MRI), Semi-Classical Signal Analysis (SCSA), adaptive image denoising, eigenfunctions of the Schrodinger operator",

author = "Abderrazak Chahid and Hacene Serrai and Eric Achten and Laleg-Kirati, {Taous Meriem}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 ; Conference date: 19-10-2017 Through 21-10-2017",

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Chahid, A, Serrai, H, Achten, E & Laleg-Kirati, TM 2017, Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator. in 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings. 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017, Torino, Italy, 10/19/17. https://doi.org/10.1109/BIOCAS.2017.8325107

Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator. / Chahid, Abderrazak; Serrai, Hacene; Achten, Eric et al.
2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-4 (2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Achten, Eric

AU - Laleg-Kirati, Taous Meriem

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N2 - Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrödinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.

AB - Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrödinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.

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KW - Semi-Classical Signal Analysis (SCSA)

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Chahid A, Serrai H, Achten E, Laleg-Kirati TM. Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator. In 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-4. (2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings). doi: 10.1109/BIOCAS.2017.8325107

Adaptive method for MRI enhancement using squared eigenfunctions of the Schrödinger operator

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