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.
Towards Estimating the Uncertainty Associated with 3D Geometry Reconstructions from Medical Image Data
Published
Author(s)
Zachary H. Levine, Karim O. Genc, Stephen M. Luke, Todd Pietiela, Ross T. Cotton, Benjamin Ache, Phillipe G. Young, Marc Horner, Kevin C. Townsand
Abstract
3D image based modeling for visualization, physics-based simulation or additive manufacturing, is becoming more common within R&D labs in academia, government and commercial industry. Computed Tomography (CT) is a common imaging modality used to obtain the internal and external 3D geometry of objects through a series of X-ray images taken from different angles to produce cross-sectional images along an axis. Levine et al (1,2) developed a fiducial reference phantom, or NIST phantom, to help control for variations in scanner settings, noise or artifacts. Ideally, the geometry of the phantom would be extracted through threshold-based segmentation using the ISO 50 standard (3). The purpose of this study is to examine the effects of image resolution on the accuracy of 3D reconstructions from idealized and real CT images of the NIST phantom.
Proceedings Title
2016 BMES/FDA Frontiers in Medical Devices Conference
Levine, Z.
, Genc, K.
, Luke, S.
, Pietiela, T.
, Cotton, R.
, Ache, B.
, Young, P.
, Horner, M.
and Townsand, K.
(2016),
Towards Estimating the Uncertainty Associated with 3D Geometry Reconstructions from Medical Image Data, 2016 BMES/FDA Frontiers in Medical Devices Conference, College Park, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920192
(Accessed October 15, 2025)