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

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 b...

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Bibliographic Details
Published in:2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018-07, Vol.2018, p.5579-5582
Main Authors: Chahid, Abderrazak, Serrai, Hacene, Achten, Eric, Laleg-Kirati, Taous-Meriem
Format: Article
Language:English
Subjects:
Eigenvalues and eigenfunctions
Image denoising
image enhancement
Magnetic resonance imaging
Measurement
MRI
Noise reduction
Regions Of Interest (ROI)
Schrödinger Operator
Semi-Classical Signal Analysis
Signal to noise ratio
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Summary: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.
ISSN:1558-4615
2694-0604
DOI:10.1109/EMBC.2018.8513615