Review of porosity uncertainty estimation methods in computed tomography dataset

Review of porosity uncertainty estimation methods in computed tomography dataset

WoS Article

X-ray computed tomography is widely used for non-destructive testing and analysis in a broad variety of fields. Its main usage is for 3D porosity identification and quantification. This can be achieved through the image segmentation of the reconstructed dataset which can have a huge impact on the porosity value. The most widely used segmentation algorithms are based on global thresholding, which takes the whole volume into account. To ensure a certain level of confidence and reproducibility of the porosity value, a thorough description of the workflow should be available with uncertainty estimation. This workflow description is often insufficient and the uncertainty missing according to a review of the literature. This work provides recommendations on how to report the processing steps for the porosity evaluation based on computed tomography data and reviews methods for the estimation of the porosity measurement uncertainty from the literature.

X-ray computed tomography is widely used for non-destructive testing and analysis in a broad variety of fields. Its main usage is for 3D porosity identification and quantification. This can be achieved through the image segmentation of the reconstructed dataset which can have a huge impact on the porosity value. The most widely used segmentation algorithms are based on global thresholding, which takes the whole volume into account. To ensure a certain level of confidence and reproducibility of the porosity value, a thorough description of the workflow should be available with uncertainty estimation. This workflow description is often insufficient and the uncertainty missing according to a review of the literature. This work provides recommendations on how to report the processing steps for the porosity evaluation based on computed tomography data and reviews methods for the estimation of the porosity measurement uncertainty from the literature.

Global thresholding; Computed tomography; Uncertainty estimation; Results comparison; Porosity evaluation; Segmentation

Global thresholding; Computed tomography; Uncertainty estimation; Results comparison; Porosity evaluation; Segmentation

JAQUES, V.; DU PLESSIS, A.; ZEMEK, M.; ŠALPLACHTA, J.; ŠTUBIANOVÁ, Z.; ZIKMUND, T.; KAISER, J.

2022

23.08.2021

IOP Publishing

0957-0233

MEASUREMENT SCIENCE and TECHNOLOGY

32

8

United Kingdom of Great Britain and Northern Ireland

1

17

17

https://iopscience.iop.org/article/10.1088/1361-6501/ac1b40

http://hdl.handle.net/11012/201651

Jaques_2021_Meas._Sci._Technol._32_122001