Conference Paper

Chi-square unbiased risk estimate for denoising magnitude MR images.

DOI: 10.1109/ICIP.2011.6115745 Conference: 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011
Source: DBLP

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.

Download full-text

Full-text

Available from: Florian Luisier, Feb 04, 2015
0 Followers
 · 
124 Views
  • Source
    [Show abstract] [Hide abstract]
    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.
  • Source
    [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
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this article we derive an unbiased expression for the expected mean-squared error associated with continuously differentiable estimators of the noncentrality parameter of a chi-square random variable. We then consider the task of denoising squared-magnitude magnetic resonance image data, which are well modeled as independent noncentral chi-square random variables on two degrees of freedom. We consider two broad classes of linearly parameterized shrinkage estimators that can be optimized using our risk estimate, one in the general context of undecimated filterbank transforms, and another in the specific case of the unnormalized Haar wavelet transform. The resultant algorithms are computationally tractable and improve upon state-of-the-art methods for both simulated and actual magnetic resonance image data.
    IEEE Transactions on Image Processing 06/2011; 21(8). DOI:10.1109/TIP.2012.2191565 · 3.11 Impact Factor