  Porous materials are widely used in different applications, in particular
they are used to create various filters. Their quality depends on parameters
that characterize the internal structure such as porosity, permeability and so
on. Computed tomography (CT) allows one to see the internal structure of a
porous object without destroying it. The result of tomography is a gray image.
To evaluate the desired parameters, the image should be segmented. Traditional
intensity threshold approaches did not reliably produce correct results due to
limitations with CT images quality. Errors in the evaluation of characteristics
of porous materials based on segmented images can lead to the incorrect
estimation of their quality and consequently to the impossibility of
exploitation, financial losses and even to accidents. It is difficult to
perform correctly segmentation due to the strong difference in voxel
intensities of the reconstructed object and the presence of noise. Image
filtering as a preprocessing procedure is used to improve the quality of
segmentation. Nevertheless, there is a problem of choosing an optimal filter.
In this work, a method for selecting an optimal filter based on attributive
indicator of porous objects (should be free from 'levitating stones' inside of
pores) is proposed. In this paper, we use real data where beam hardening
artifacts are removed, which allows us to focus on the noise reduction process
