Chi-square unbiased risk estimate for denoising magnitude MR images.
ABSTRACT In this article we develop Stein-type results for unbiased estimation of the risk associated with parametric estimators of the noncentrality parameter of chi-squared random variables on two degrees of freedom. These results allow for estimator adaptivity, and thus can be used to optimize the parameters of a broad class of typical denoising functions, subject only to weak smoothness assumptions. We show how to apply these results to the problem of enhancing magnitude magnetic resonance images, which are known to be corrupted by Rician noise. As an example, we propose a transform-domain point-wise estimator based on linear expansion of thresholds. Finally, we apply this estimator to synthetic and real image data in conjunction with the undecimated Haar wavelet transform, and conclude that it is able to outperform previous wavelet-based techniques and compares favorably with a more recent approach based on non-local means.
Full-textDOI: · Available from: Florian Luisier, Feb 04, 2015
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Conference Paper: Magnitude MR image denoising via CURE-optimized non-local means[Show abstract] [Hide abstract]
ABSTRACT: During their acquisition, magnetic resonance (MR) images are affected by random noise, causing the observed magnitude image samples to be Rician distributed. In order to reduce the noise level while preserving the relevant image features, we develop an optimized Non-Local Means (NLM) denoising algorithm. The most sensitive parameters of the proposed NLM estimator are optimized on the squared-magnitude image, which follows a non-central chi-square distribution on two degrees of freedom. This minimum MSE optimization is performed via the minimization of the so-called chi-square unbiased risk estimate (CURE). Taking advantage of some acceleration techniques involving convolutions and parallel computation, we show that the proposed CURE-optimized NLM outperforms some state-of-the-art NLM algorithms with no increase in computation time.Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on; 01/2012
Article: Directional Bilateral Filters[Show abstract] [Hide abstract]
ABSTRACT: We propose a bilateral filter with a locally controlled domain kernel for directional edge-preserving smoothing. Traditional bilateral filters use a range kernel, which is responsible for edge preservation, and a fixed domain kernel that performs smoothing. Our intuition is that orientation and anisotropy of image structures should be incorporated into the domain kernel while smoothing. For this purpose, we employ an oriented Gaussian domain kernel locally controlled by a structure tensor. The oriented domain kernel combined with a range kernel forms the directional bilateral filter. The two kernels assist each other in effectively suppressing the influence of the outliers while smoothing. To find the optimal parameters of the directional bilateral filter, we propose the use of Stein's unbiased risk estimate (SURE). We test the capabilities of the kernels separately as well as together, first on synthetic images, and then on real endoscopic images. The directional bilateral filter has better denoising performance than the Gaussian bilateral filter at various noise levels in terms of peak signal-to-noise ratio (PSNR).
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ABSTRACT: Segmentation plays a vital role in extracting information from medical images .Segmentation is the process of partitioning the image into distinct regions. Magnetic resonance imaging is used to extract images of soft tissues of human body. It is used to analyze the human organs without the need for surgery. Generally MRI images contain a significant amount of noise caused by operator performance, equipment and the environment, which leads to serious inaccuracies MRI seems to be efficient in providing information regarding the location of tumors and even the volume. The noise present in the MRI image can be removed by using various de-noising techniques whichever is best suited depending upon the image acquired and then can be processed by any of the segmentation methods. The noise in MRI images may be due to field strength, RF pulses, RF coil, voxel volume, or receiver bandwidth. A review of various de-noising methods are presented.