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A critical review of the effects of de-noising algorithms on MRI brain tumor segmentation

Idanis Diaz
Pierre Boulanger
Russ Greiner, Dept of Computing Science; PI of AICML
Albert Murtha, Cross Cancer Institute, Edmonton

One can find in the literature numerous techniques to reduce noise in Magnetic Resonance Images (MRI). This paper critically reviews modern de-noising algorithms (Gaussian filter, anisotropic diffusion, wavelet, and non-local mean) in terms of their efficiency, statistical assumptions, and their ability to improve brain tumor segmentation results. We will show that although different techniques do reduce the noise, many generate artifacts that are incompatible with precise brain tumor segmentation. We also show that the non-local means algorithm is the best de-noising technique for brain tumor segmentation.

Citation

I. Diaz, P. Boulanger, R. Greiner, A. Murtha. "A critical review of the effects of de-noising algorithms on MRI brain tumor segmentation". IEEE Engineering in Medicine and Biology Society Conference, October 2011.

Keywords:	MRI, brain tumors, brain imaging
Category:	In Conference
Web Links:	Conference URL

BibTeX

@incollection{Diaz+al:IEEE-Medicine11,
  author = {Idanis Diaz and Pierre Boulanger and Russ Greiner and Albert
    Murtha},
  title = {A critical review of the effects of de-noising algorithms on MRI
    brain tumor segmentation},
  booktitle = {IEEE Engineering in Medicine and Biology Society Conference},
  year = 2011,
}

Last Updated: October 25, 2016
Submitted by Russ Greiner

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A critical review of the effects of de-noising algorithms on MRI brain tumor segmentation

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