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Towards Estimating the Uncertainty Associated with 3D Geometry Reconstructions from Medical Image Data



Zachary H. Levine, Karim O. Genc, Stephen M. Luke, Todd Pietiela, Ross T. Cotton, Benjamin Ache, Phillipe G. Young, Marc Horner, Kevin C. Townsand


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
Conference Dates
May 25-27, 2016
Conference Location
College Park, MD


medical CT , NIST phantom


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], (Accessed June 23, 2024)


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Created May 23, 2016, Updated May 9, 2019