Abstract
Abstract Denoising is always a challenging problem in magnetic resonance imaging (MRI) and is important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. The noise in MRI has a Rician distribution. Unlike additive Gaussian noise, Rician noise is signal dependent, and separating the signal from the noise is a difficult task. In this paper, we propose a useful alternative of the nonlocal mean (NLM) filter that uses nonparametric principal component analysis (NPCA) for Rician noise reduction in MR images. This alternative is called the NPCA-NLM filter, and it results in improved accuracy and computational performance. We present an applicable method for estimating smoothing kernel width parameters for a much larger set of images and demonstrate that the number of principal components for NPCA is robust to variations in the noise as well as in images. Finally, we investigate the performance of the proposed filter with the standard NLM filter and the PCA-NLM filter on MR images corrupted with various levels of Rician noise. The experimental results indicate that the NPCA-NLM filter is the most robust to variations in images, and shows good performance at all noise levels tested.
Highlights
Magnetic resonance (MR) images are affected by several types of artifact and noise sources, such as random fluctuations in the MR signal mainly due to the thermal vibrations of ions and electrons
We propose a nonparametric principal component analysis (PCA)-nonlocal means (NLM) filter that is a useful alternative to the PCA-NLM filter for Rician noise reduction in MR images
5 Conclusion We proposed an nonparametric principal component analysis (NPCA)-NLM filter, which is a useful alternative to the PCA-NLM filter for Rician noise reduction in MR images
Summary
Magnetic resonance (MR) images are affected by several types of artifact and noise sources, such as random fluctuations in the MR signal mainly due to the thermal vibrations of ions and electrons. Most conventional mask-based denoising filters, such as Gaussian and Wiener filters [4], are conceptually simple They will most likely fail to reduce Rician noise in MRI, as they usually assume that the noise is. Et al [11] and Tasdizen [13,14] proposed the so-called PCA-NLM filter, which uses the lower dimensional subspace of the space of image neighborhood vectors in conjunction with NLM using principal component analysis (PCA). We propose a nonparametric method for optimal smoothing kernel width selection
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