TY - GEN
T1 - A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator
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.
UR - http://hdl.handle.net/10754/630099
UR - https://ieeexplore.ieee.org/document/8513615
UR - http://www.scopus.com/inward/record.url?scp=85056659487&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513615
DO - 10.1109/EMBC.2018.8513615
M3 - Conference contribution
SN - 9781538636466
SP - 5579
EP - 5582
BT - 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -