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dc.contributor.authorChahid, Abderrazak
dc.contributor.authorSerrai, Hacene
dc.contributor.authorAchten, Eric
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2020-05-11T15:34:05Z
dc.date.available2020-05-11T15:34:05Z
dc.identifier.urihttp://hdl.handle.net/10754/662797
dc.description.abstractIn this paper, an adaptive method for Magnetic Resonance (MR) image denoising is proposed, based on the Semi-Classical Signal Analysis (SCSA). The SCSA employs the squared eigenfunctions of the Schrodinger operator, whose potential is considered to be the noisy image. However, the low performance of the method, using single-valued parameters $h$ and $\gamma$, is mainly due to the non-uniform distribution of the noise in the MR image. This non-uniformity is related to multiple factors such as the used modality, electrical noise, and other artifacts related to the patient. To overcome this challenge, the proposed adaptive SCSA algorithm locally optimizes the pair (h,gamma), using a grid segmentation, to introduce an appropriate distribution along the different sub-images of the grid. This adaptation to the spatial variation of noise responds efficiently to the denoising objectives. Numerical tests using synthetic datasets from BrainWeb and real MR images show the effectiveness of the proposed method. The obtained results are also compared to the conventional case.
dc.subjectMagnetic Resonance Imaging (MRI)
dc.subjectadaptive image denoising
dc.subjectSemi-Classical Signal Analysis (SCSA)
dc.subjecteigenfunctions of the Schrodinger operator
dc.titleLocally Enhanced Denoising Method for MRI Imaging using the Schrodinger Operator
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)
dc.contributor.institutionRobarts research institute, University of Western Ontario, London, Ontario Canada
dc.contributor.institutionDepartment of Diagnostic Sciences, University of Ghent, Gent, BE
refterms.dateFOA2020-05-11T15:34:06Z


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