Performance Analysis of Intensity Averaging By Anisotropic Diffusion Method for MRI Denoising Corrupted By Random Noise
The two parameters which plays important role in MRI(magnetic resonance imaging),acquired by various imaging modalities are Feature extraction and object recognition. These operations will become difficult if the images are corrupted with noise. Noise in MR image is always random type of noise. This noise will change the value of amplitude and phase of each pixel in MR image. Due to this, MR image gets corrupted and we cannot make perfect diagnostic for a body. So noise removal is essential task for perfect diagnostic. There are different approaches for noise reduction, each of which has its own advantages and limitation. MRI denoising is a difficult task task as fine details in medical image containing diagnostic information should not be removed during noise removal process. In this paper, we are representing an algorithm for MRI denoising in which we are using iterations and Gaussian blurring for amplitude reconstruction and image fusion,anisotropic diffusion and FFT for phase reconstruction. We are using the PSNR(Peak signal to noise ration),MSE(Mean square error) and RMSE(Root mean square error) as performance matrices to measure the quality of denoised MRI. The final result shows that this method is effectively removing the noise while preserving the edge and fine information in the images.
