Share Email Print

Proceedings Paper

Classification and performance of denoising algorithms for low signal-to-noise ratio magnetic resonance images
Author(s): Wilfred L. Rosenbaum; M. Stella Atkins; Gordon E. Sarty
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The generation of magnitude magnetic resonance images comprises a sequence of data encodings or transformations, from detection of an analog electrical signal to a digital phase/frequency k-space to a complex image space via an inverse Fourier transform and finally to a magnitude image space via a magnitude transformation and rescaling. Noise present in the original signal is transformed at each step of this sequence. Denoising MR images from low field strength scanners is important because such images exhibit low signal to noise ratio. Algorithms that perform denoising of magnetic resonance images may be usefully classified according to the data domain on which they operate (i.e. at which step of the sequence of transformations they are applied) and the underlying statistical distribution of the noise they assume. This latter dimension is important because the noise distribution for low SNR images may be decidedly non-Gaussian. Examples of denoising algorithms include 2D wavelet thresholding (operates on the wavelet transform of the magnitude image; assumes Gaussian noise), Nowak's 2D wavelet filter (operates on the squared wavelet transform of the magnitude image; assumes Rician noise), Alexander et. al.'s complex 2D filters (operates on the wavelet transform of the complex image space; assumes Gaussian noise), wavelet packet denoising (wavelet packet transformation of magnitude image; assumes Rician noise) and anisotropic diffusion filtering (operates directly on magnitude image; no assumptions on noise distribution). Effective denoising of MR images must take into account both the availability of the underlying data, and the distribution of the noise to be removed. We classify a number of recently published denoising algorithms and compare their performance on images from a 0.35T permanent magnet MR scanner.

Paper Details

Date Published: 6 June 2000
PDF: 7 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387655
Show Author Affiliations
Wilfred L. Rosenbaum, Simon Fraser Univ. (Canada)
M. Stella Atkins, Simon Fraser Univ. (Canada)
Gordon E. Sarty, Univ. of Saskatchewan (Canada)

Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?