BMVC 2004, Kingston, 7th-9th Sept, 2004

Noise Filtering and Testing Illustrated Using a Multi-Dimensional Partial Volume Model of MR Data
N.A. Thacker, M. Pokric and D.C. Williamson (University of Manchester)

One of the most common problems in image analysis is the estimation
and removal of noise or other artefacts using spatial filters. Common techniques
include Gaussian, Median and Anisotropic Filtering. Though these
techniques are quite common they must be used with great care on medical
data, as it is very easy to introduce artifact into images due to spatial
smoothing. The use of such techniques is further restricted by the absence
of a `gold standard' data against which to test the behaviour of the filter.
Following a general discussion of the equivalence of filtering techniques to
likelihood based estimation using an assumed model, this paper describes an
approach to noise filtering in multi-dimensional data using a partial volume
data density model. The resulting data sets can then be taken as a gold standard
for spatial filtering techniques which use the information from single
images. We demonstrate equivalence between the results from this analysis
and techniques for performance characterisation which do not require a `gold
(pdf article)