Biomedical Images Enhancement

Biomedical images enhancement using optimized Schrödinger operator-based method

​​The accepted paper will be presented by Abderrazak Chahid at the 13th IEEE BioCAS, to be held on October 19-21 in in Turin, Italy. Abderrazak will present a new adaptive enhancement method for Magnetic Resonance Imaging (MRI) image. The new method is based on an adaptive selection of a filtering parameter by a grid segmentation of the noisy input image (Fig.1). The filtering parameter, called h, 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.
 The method has been applied to a synthetic dataset from BrainWeb (Fig.2) and real MRI images (Fig.3) show the effectiveness of the proposed approach compared to the standard case with one fixed parameter.
These preliminary results provide very encouraging insight and show good potential in MRI noise removal while preserving the small details.

 Reference :

Abderrazak Chahid, Hacene Serrai, Eric Achten, Taous-Meriem Laleg-Kirati, “Adaptive Method for MRI Enhancement Using Squared Eigenfunctions of the Schrödinger Operator," accepted in BIOCAS2017, Italy, 2017.