A Hybrid Filter for Denoising of MRI Brain Images using Fast Independent Component Analysis

Abstract

Denoising of MRI images is very essential for the effective diagnosis of various brain diseases. In this paper, a new hybrid ROFTV-Fast ICA algorithm is proposed to enhance the MRI images corrupted by Gaussian noise. The original MRI image is subjected to Gaussian noise. The corrupted brain image is denoised by the combination of both Rudin-Osher- Fatemi (ROF) Total variation filter and Fast-Independent Component Analysis (ICA) algorithms. The total variation in the noisy brain images is minimized by using ROFTV filter. Again, the recovered image is denoised further by Fast ICA algorithm, by separating the noise and noiseless components in the image. The performance of this hybrid ROFTV -Fast ICA filter is evaluated by means of Peak Signal to Noise Ratio (PSNR). The proposed method is also compared with Adaptive Median Filter (AMF), Progressive Switching Median Filter (PSMF) and Bilateral filter (BF). The result shows that the proposed hybrid algorithm outperforms rest of the filters and smoothens the MRI images very well also preserving the edges and corners.