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Porous materials are widely used in different applications, in particularthey are used to create various filters. Their quality depends on parametersthat characterize the internal structure such as porosity, permeability and soon. Computed tomography (CT) allows one to see the internal structure of aporous object without destroying it. The result of tomography is a gray image.To evaluate the desired parameters, the image should be segmented. Traditionalintensity threshold approaches did not reliably produce correct results due tolimitations with CT images quality. Errors in the evaluation of characteristicsof porous materials based on segmented images can lead to the incorrectestimation of their quality and consequently to the impossibility ofexploitation, financial losses and even to accidents. It is difficult toperform correctly segmentation due to the strong difference in voxelintensities of the reconstructed object and the presence of noise. Imagefiltering as a preprocessing procedure is used to improve the quality ofsegmentation. Nevertheless, there is a problem of choosing an optimal filter.In this work, a method for selecting an optimal filter based on attributiveindicator of porous objects (should be free from 'levitating stones' inside ofpores) is proposed. In this paper, we use real data where beam hardeningartifacts are removed, which allows us to focus on the noise reduction process

Segmentation Criteria in the Problem of Porosity Determination based on CT Scans