Adaptive anisotropic noise filtering for magnitude MR data

  title={Adaptive anisotropic noise filtering for magnitude MR data},
  author={Jan Sijbers and Arnold Jan den Dekker and Marleen Verhoye and Annemarie van der Linden and Dirk Van Dyck},
  booktitle={Medical Imaging},
In general, conventional noise filtering schemes applied to magnitude magnetic resonance (MR) images are based on Gauss distributed noise. Magnitude MR data, however, are Rice distributed. Not incorporating this knowledge leads inevitably to biased results, in particular when applying those filters in regions with low signal-to-noise ratio. In this work, we show how the Rice data probability distribution can be incorporated to construct a noise filter that is far less biased. 

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Segmentation based non local means filter for denoising MRI

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  • Engineering
    2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
  • 2017
A novel method for reducing the effect of noise by amalgamating segmentation technique with a group of denoising filters is suggested.



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In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed, which overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details.

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This report describes how to extract true intensity measurements in the presence of noise in magnetic resonance imaging.

Adaptive filtering for high resolution magnetic resonance images

The AFLME and AFEN filters both show approximately a factor of 2 SNR improvement with much better retention of edges, with AFEN showing slightly better performance for both SNR and edge sharpness in phantom and in vivo inner ear images.

Parameter estimation from magnitude MR images

This paper deals with the estimation of model-based parameters, such as the noise variance and signal components, from magnitude Magnetic Resonance (MR) images, and proposes a method based on Maximum Likelihood (ML) estimation.

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  • Yue WangT. Lei
  • Mathematics
    Proceedings of 1st International Conference on Image Processing
  • 1994
The Gaussianity, stationarity, dependence and ergodicity of MR image random field are characterized as the standard problems of statistics, and justified to form the basis for establishing the stochastic image model and conducting the statistical image analysis.

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In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.