NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Local Thickness and Anisotropy Approaches to Characterize Pore Size Distribution of 3D Porous Networks
Published
Author(s)
Martin Y. Chiang, Xianfeng Wang
Abstract
We have proposed a superseding algorithm approach for image analysis of the pore size distribution (PSD) of porous media. This approach adapts the intrinsic definition of skeletonization for an object representation to the characterization of the PSD, and consequently is independent of the analytical technique.The proposed superseding algorithm was applied to a synthetic 2D and a natural 3D image for extracting PSDs. Also, comparisons of the PSD results were made between the proposed approach and the chord-length distribution approach, which has been a tool in the characterization of porous and composite media. Our study indicates that the proposed algorithm is reliable and efficient to evaluate the pore size distribution including local details for porous media with complicated morphologies of any scale. In addition, the skeleton which results during the superseding process can be used to obtain geometrical and morphological characteristics in many other applications.
Chiang, M.
and Wang, X.
(2009),
Local Thickness and Anisotropy Approaches to Characterize Pore Size Distribution of 3D Porous Networks, Tissue Engineering, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=852504
(Accessed October 7, 2025)