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

TL;DR

This paper proposes a new method for selecting optimal filters for segmenting CT scan images of porous materials, improving accuracy in porosity determination crucial for material quality assessment.

Contribution

A novel filter selection method based on an attributive indicator that avoids artifacts, enhancing segmentation reliability in CT images of porous structures.

Findings

The proposed method improves segmentation accuracy.

It effectively reduces noise without introducing artifacts.

Application on real data demonstrates practical effectiveness.

Abstract

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