Abstract
The nonlocal means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM filter based on the discrete cosine transform (DCT). Instead of computing similarity weights using the gray level information directly, the proposed method calculates similarity weights in the DCT subspace of neighborhood. Due to promising characteristics of DCT, such as low data correlation and high energy compaction, the proposed filter is naturally endowed with more accurate estimation of weights thus enhances denoising effectively. The performance of the proposed filter is evaluated qualitatively and quantitatively together with two other NLM filters, namely, the original NLM filter and the unbiased NLM (UNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance in MRI compared to the others.
Highlights
Magnetic resonance imaging (MRI) is one of the most powerful imaging techniques [1] developed to study the structural features and the functional characteristics of the internal body parts
The results show that the unbiased NLM (UNLM)-discrete cosine transform (DCT) filter outperforms the nonlocal means (NLM) filter and UNLM filter among peak signal noise ratio (PSNR) value, vision, PSNR PSNR
H = 4.22.1, which is assigned according to the estimated noise standard deviation of the knee magnetic resonance (MR) image
Summary
Magnetic resonance imaging (MRI) is one of the most powerful imaging techniques [1] developed to study the structural features and the functional characteristics of the internal body parts. The visual quality of the MR images is normally corrupted by random noise from the acquisition process. Such a noise in MRI is mainly due to thermal noise that is induced by the movement of the charged particles in the radio frequency coils as well as the small anomalies in the preamplifiers. Noise in MRI limits the visual inspection and the computer-aided analysis of these images. Instead, filtering methods have been traditionally applied in the postprocessing stages Such filtering methods have the drawback that, while removing noise, they may remove high frequency signal components, thereby blurring the edges in the image and introducing some bias in the quantification process
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