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Dimensional Quantification of Embedded Voids or Objects in Three Dimensions Using X-Ray Tomography

Published online by Cambridge University Press:  03 February 2012

Brian M. Patterson ,
Juan P. Escobedo-Diaz ,
Darcie Dennis-Koller  and
Ellen Cerreta
Brian M. Patterson*
Affiliation:
Los Alamos National Laboratory, Materials Science and Technology Division, P.O. Box 1663, MS E549, Los Alamos, New Mexico 87545, USA
Juan P. Escobedo-Diaz
Affiliation:
Los Alamos National Laboratory, Materials Science and Technology Division, P.O. Box 1663, MS G755, Los Alamos, New Mexico 87545, USA
Darcie Dennis-Koller
Affiliation:
Los Alamos National Laboratory, Materials Science and Technology Division, P.O. Box 1663, MS P952, Los Alamos, New Mexico 87545, USA
Ellen Cerreta
Affiliation:
Los Alamos National Laboratory, Materials Science and Technology Division, P.O. Box 1663, MS G755, Los Alamos, New Mexico 87545, USA
*
Corresponding author. E-mail: [email protected]
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Abstract

Scientific digital imaging in three dimensions such as when using X-ray computed tomography offers a variety of ways to obtain, filter, and quantify data that can produce vastly different results. These opportunities, performed during image acquisition or during the data processing, can include filtering, cropping, and setting thresholds. Quantifying features in these images can be greatly affected by how the above operations are performed. For example, during binarization, setting the threshold too low or too high can change the number of objects as well as their measured diameter. Here, two facets of three-dimensional quantification are explored. The first will focus on investigating the question of how many voxels are needed within an object to have accurate geometric statistics that are due to the properties of the object and not an artifact of too few voxels. These statistics include but are not limited to percent of total volume, volume of the individual object, Feret shape, and surface area. Using simple cylinders as a starting point, various techniques for smoothing, filtering, and other processing steps can be investigated to aid in determining if they are appropriate for a specific desired statistic for a real dataset. The second area of investigation is the influence of post-processing, particularly segmentation, on measuring the damage statistics in high purity Cu. The most important parts of the pathways of processing are highlighted.

Keywords

X-ray tomography3D quantificationcopper spall3D statistics
Type
Techniques and Software Development
Information
Microscopy and Microanalysis , Volume 18 , Issue 2 , April 2012 , pp. 390 - 398
Copyright
Copyright © Microscopy Society of America 2012

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