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 filters. 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 standard'.