Removal of rician noise in MRI images using bilateral filter by fuzzy trapezoidal membership function

Magnetic resonance images (MRI) plays a crucial role in neuroscience and medical diagnosis. Denoising MRI images is an important preprocessing step required in many of the automatic computed aided-diagnosis systems in neuroscience. Rician noise occurs in the MRI image during acquisition. Non local mean filter is used for denoising. But the parameter selection is not optimized. The proposed method removal of rician noise in MRI images using bilateral filter by fuzzy trapezoidal membership function improves the denoising efficiency at various noise variances, preserves the fine structures and edges. The fuzzy weights were obtained with the statistical features such as local mean (mu(i)) and global mean (mu(g)) by constructing trapezoidal membership function. Bilateral filter is used to preserve the edges by smoothening the noises in MRI image and preserves the structural information. Local filter preserves the edges. MRI images are restored by multiplying its corresponding fuzzy weight with the restored image of local order filter and bilateral filter. Experiments on simulated and real MRI data were done at different noise levels by the proposed method and the existing methods. The result shows that the proposed method restores the image in better visual quality and can be well utilized for diagnostic purpose at both low and high densities of rician noise.