A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

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

doi:10.1109/EMBC.2018.8513615

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

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

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

12 Scopus citations

Abstract

In this paper a new Region Of Interest (ROI) characterization for image denoising performance evaluation is proposed. This technique consists of balancing the contrast between the dark and bright ROIs, in Magnetic Resonance (MR) images, to track the noise removal. It achieves an optimal compromise between removal of noise and preservation of image details. The ROI technique has been tested using synthetic MRI images from the BrainWeb database. Moreover, it has been applied to a recently developed denoising method called Semi-Classical Signal Analysis (SCSA). The SCSA decomposes the image into the squared eigenfunctions of the Schrödinger operator where a soft threshold h is used to remove the noise. The results obtained using real MRI data suggest that this method is suitable for real medical image processing evaluation where the noise-free image is not available.

Original language	English (US)
Title of host publication	2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	5579-5582
Number of pages	4
ISBN (Print)	9781538636466
DOIs	https://doi.org/10.1109/EMBC.2018.8513615
State	Published - Nov 16 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology.

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10.1109/EMBC.2018.8513615

https://repository.kaust.edu.sa/bitstream/10754/630099/3/EMBC18_ROI_Deoising_EMBC2018_Chahid1.pdf

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Chahid, A., Serrai, H., Achten, E., & Laleg-Kirati, T.-M. (2018). A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5579-5582). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2018.8513615

Chahid, Abderrazak ; Serrai, Hacene ; Achten, Eric et al. / A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), 2018. pp. 5579-5582

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title = "A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schr{\"o}dinger Operator",

abstract = "In this paper a new Region Of Interest (ROI) characterization for image denoising performance evaluation is proposed. This technique consists of balancing the contrast between the dark and bright ROIs, in Magnetic Resonance (MR) images, to track the noise removal. It achieves an optimal compromise between removal of noise and preservation of image details. The ROI technique has been tested using synthetic MRI images from the BrainWeb database. Moreover, it has been applied to a recently developed denoising method called Semi-Classical Signal Analysis (SCSA). The SCSA decomposes the image into the squared eigenfunctions of the Schr{\"o}dinger operator where a soft threshold h is used to remove the noise. The results obtained using real MRI data suggest that this method is suitable for real medical image processing evaluation where the noise-free image is not available.",

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

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Chahid, A, Serrai, H, Achten, E & Laleg-Kirati, T-M 2018, A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), pp. 5579-5582. https://doi.org/10.1109/EMBC.2018.8513615

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator. / Chahid, Abderrazak; Serrai, Hacene; Achten, Eric et al.
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 5579-5582.

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

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AU - Chahid, Abderrazak

AU - Serrai, Hacene

AU - Achten, Eric

AU - Laleg-Kirati, Taous-Meriem

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology.

PY - 2018/11/16

Y1 - 2018/11/16

N2 - In this paper a new Region Of Interest (ROI) characterization for image denoising performance evaluation is proposed. This technique consists of balancing the contrast between the dark and bright ROIs, in Magnetic Resonance (MR) images, to track the noise removal. It achieves an optimal compromise between removal of noise and preservation of image details. The ROI technique has been tested using synthetic MRI images from the BrainWeb database. Moreover, it has been applied to a recently developed denoising method called Semi-Classical Signal Analysis (SCSA). The SCSA decomposes the image into the squared eigenfunctions of the Schrödinger operator where a soft threshold h is used to remove the noise. The results obtained using real MRI data suggest that this method is suitable for real medical image processing evaluation where the noise-free image is not available.

AB - In this paper a new Region Of Interest (ROI) characterization for image denoising performance evaluation is proposed. This technique consists of balancing the contrast between the dark and bright ROIs, in Magnetic Resonance (MR) images, to track the noise removal. It achieves an optimal compromise between removal of noise and preservation of image details. The ROI technique has been tested using synthetic MRI images from the BrainWeb database. Moreover, it has been applied to a recently developed denoising method called Semi-Classical Signal Analysis (SCSA). The SCSA decomposes the image into the squared eigenfunctions of the Schrödinger operator where a soft threshold h is used to remove the noise. The results obtained using real MRI data suggest that this method is suitable for real medical image processing evaluation where the noise-free image is not available.

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Chahid A, Serrai H, Achten E, Laleg-Kirati TM. A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE). 2018. p. 5579-5582 doi: 10.1109/EMBC.2018.8513615

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

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