Modified Anisotropic Diffusion filtering algorithm for MRI

During the acquisition process of Magnetic Resonance Imaging (MRI), irregular bias is imposed in the intensity values of the pixels. These biases follow the Gaussian Noise distribution model and act as a constraint to the effective medical diagnosis. The conventional Anisotropic Diffusion (AD) approach is limited to preserve the structural integrity of MRI at only low noise levels. This paper proposes a modified AD algorithm aimed to improve the estimation of the diffusion constant to facilitate better edge detection and preservation of details. The proposed algorithm operates on the decomposed mask images of MRI by incorporating the domain filtering principle of the Bilateral filter(prior to the estimation of diffusion constant). Simulation trials have been conducted at different Gaussian noise variances and performance has been evaluated on the basis of Peak Signal-Noise Ratio (PSNR) and Structural Similarity (SSIM). The proposed algorithm has shown stable value of evaluation parameters at higher noise variances. Also, the preservation of details has improved as compared to the conventional AD approach.