Multicomponent MR Image Denoising
Magnetic Resonance images are normally corrupted by random noise from
the measurement process complicating the automatic feature extraction
and analysis of clinical data. It is because of this reason that
denoising methods have been traditionally applied to improve MR image
quality. Many of these methods use the information of a single image
without taking into consideration the intrinsic multicomponent nature
of MR images. In this paper we propose a new filter to reduce random
noise in multicomponent MR images by spatially averaging similar pixels
using information from all available image components to perform the
denoising process. The proposed algorithm also uses a local Principal
Component Analysis decomposition as a post-processing step to remove
more noise by using information not only in the spatial domain but also
in the inter-component domain dealing in a higher noise reduction
without significantly affecting the original image resolution. The
proposed method has been compared with similar state of art methods
over synthetic and real clinical multicomponent MR images showing an
improved performance in all cases analyzed.
Details can be found in:
Manjón J.V., Thacker N.A., Lull J.J., Garcia-Martí G., Martí-Bonmatí L., Robles M. Multicomponent MR Image Denoising. International Journal of Biomedical imaging. vol 2009. Article ID 756897. 2009.
Demo data and the source code of the proposed method can be found here.
Version 1.0. (September 2009)