title: A critical review of the effects of de-noising algorithms on MRI brain tumor segmentation.
abstract: 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.
authors.affiliation: Dept of Comput Science, U of Alberta, Canada. idanis@ualberta.ca
authors.forename: Idanis
authors.initials: I
authors.lastname: Diaz
authors.forename: Pierre
authors.initials: P
authors.lastname: Boulanger
authors.forename: Russell
authors.initials: R
authors.lastname: Greiner
authors.forename: Albert
authors.initials: A
authors.lastname: Murtha
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*