TY - GEN
T1 - Adaptive method for MRI enhancement using 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 2021-02-19
PY - 2018/4/11
Y1 - 2018/4/11
N2 - Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrödinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.
AB - Recently, a Magnetic Resonance image denoising method, based on squared eigenfunctions of the Schrödinger operator, has been presented. However, its performance depends on the choice of a filtering parameter called h. We propose an adaptive selection of the filtering parameter by a grid segmentation of the noisy input image. The latter will follow an appropriate distribution along the different sub-images allowing the adaptation of its value to the spatial variation of noise and responded efficiently to the denoising objectives. Numerical tests using a synthetic dataset from BrainWeb and real MR images show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.
UR - http://hdl.handle.net/10754/630098
UR - http://ieeexplore.ieee.org/document/8325107/
UR - http://www.scopus.com/inward/record.url?scp=85049977617&partnerID=8YFLogxK
U2 - 10.1109/BIOCAS.2017.8325107
DO - 10.1109/BIOCAS.2017.8325107
M3 - Conference contribution
AN - SCOPUS:85049977617
SN - 9781509058037
SP - 1
EP - 4
BT - 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)
PB - IEEE
ER -