A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator
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EMBC18_ROI_Deoising_EMBC2018_Chahid1.pdf
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Accepted Manuscript
Type
Conference PaperKAUST Department
Computational Bioscience Research Center (CBRC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Date
2018-11-16Online Publication Date
2018-11-16Print Publication Date
2018-07Permanent link to this record
http://hdl.handle.net/10754/630099
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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.Citation
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. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Available: http://dx.doi.org/10.1109/EMBC.2018.8513615.Sponsors
Research reported in this publication was supported by King Abdullah University of Science and Technology.Conference/Event name
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Additional Links
https://ieeexplore.ieee.org/document/8513615ae974a485f413a2113503eed53cd6c53
10.1109/EMBC.2018.8513615