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 proceedingConference contribution

1 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 languageEnglish (US)
Title of host publication2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5579-5582
Number of pages4
ISBN (Print)9781538636466
DOIs
StatePublished - Nov 16 2018

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