Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài : Rician nonlocal means denoising for MR images using nonparametric principal component analysis | Kim et al. EURASIP Journal on Image and Video Processing 2011 2011 15 http content 2011 1 15 D EURASIP Journal on Image and Video Processing a SpringerOpen Journal RESEARCH Open Access Rician nonlocal means denoising for MR images using nonparametric principal component analysis Dong Wook Kim1 Chansoo Kim2 Dong Hee Kim1 and Dong Hoon Lim3 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. Keywords image denoising magnetic resonance MR image nonlocal means NLM nonparametric principal component analysis NPCA Rician noise 1 Introduction 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. Such noise markedly degrades the acquisition of quantitative .
