A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images
Denoising of Magnetic Resonance images is important for proper visual analysis, accurate parameter estimation, and for further preprocessing of these images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising Magnetic Resonance (MR) images. Among the ML based methods, the recently proposed Non Local Maximum Likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation and, as a result, optimal results cannot be achieved because of over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness.
KeywordsImage denoising Kolmogorov-Smirnov test MRI Noise Rice distribution
- 4.Cocosco, C.A., Kollokian, V., Kwan, R.-S., Evans, A.C.: Brainweb: Online interface to a 3D MRI simulated brain database. NeuroImage 5(4), S425 (1997), http://www.bic.mni.mcgill.ca/brainweb/
- 10.Rajan, J., Poot, D., Juntu, J., Sijbers, J.: Noise measurement from magnitude MRI using local estimates of variance and skewness. Phys. Med. Biol. 55, N441–N449 (2010)Google Scholar
- 19.Zimmer, S., Didas, S., Weickert, J.: A rotationally invariant block matching strategy improving image denoising with non-local means. In: International Workshop on Local and Non-Local Approximation in Image Processing, Switzerland, pp. 135–142 (2008)Google Scholar