Adaptive Non-Local Means Denoising of MR Images with Spatially Varying Noise Levels
Image filtering techniques are usually
applied to increase the quality of MR images. Most of these techniques
assume
an equal noise distribution across the image. When this assumption is
not met,
the resulting filtering becomes suboptimal. This is the case of MR
images with
spatially varying noise levels, such as those obtained by parallel
imaging (sensitivity-encoded),
intensity inhomogeneity-corrected images or surface coil based
acquisitions. In this work, we have adapted a recently proposed filter so-called
Non-Local Means
(NL-means) to deal with MR images with spatially varying noise levels
(for both
Gaussian and Rician distributed noise). With this new method,
information
regarding the local image noise level is used to adjust the amount of
denoising
strength of the filter. Such information is automatically obtained from
the
images by using a new local noise estimation method. The proposed
method has
been validated and compared with the standard NL-means filter on
simulated and real
MR imaging data. The new noise-adaptive NL-means method was
demonstrated to
outperform the standard NL-means based filter when spatially varying
noise is
present in the images.
Details can be found in:
Manjón J.V., Coupé P., Martí-Bonmatí L., Robles M., Collins D.L. Adaptive Non-Local Means Denoising of MR Images with Spatially Varying Noise Levels. Journal of Magnetic Resonance Imaging, 31,192-203, 2010.
Demo data and the source code of the proposed method can be found here.
Version 1.0. (February 2010)
Version 2.0. Multiplatform (July 2012)