Fusion Based MR Images Denoising Technique Using Frequency Domain and Non-Local Means Filters

Abstract

The non-invasive and non-ionizing properties of Magnetic Resonance Imaging (MRI) in addition to the associated good image quality as well as high resolution make MRI more attractive than many other medical imaging techniques. However, during the acquisition, transmission, compression and storage processes, the Magnetic Resonance (MR) images are corrupted by various types of noise and artifacts that degrade their visual quality. Most of the existing MR images denoising techniques give good quality images only when the noise density is low with their performances deteriorating as the noise power increases. The few methods that yield high quality images for all noise densities involve multiple complex and time-consuming processes. This paper proposes a computationally simple MR images denoising technique that consistently gives good denoising results for low as well as high noise densities. The proposed procedure fuses an MR image that is denoised by a Modified Discrete Fast Fourier Transform (MDFFT) filter with one that is denoised using a non-local means filter in frequency domain to yield a high quality output image. The main contribution of this proposed method is the employment of a novel image fusion approach that greatly improves the quality of the denoised image. The performance of the proposed technique is compared with those of the Wiener, median, adaptive median and the MDFFT filters. Objective metrics such as the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity (SSIM) index were used in the performance assessments. The outcomes of these assessments showed that the proposed algorithm yielded images of higher quality in terms of the PSNR measure than the existing denoising techniques by at least 7.11 dB for a noise density of up to 0.5.