About this Abstract |
Meeting |
5th International Congress on 3D Materials Science (3DMS 2021)
|
Symposium
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5th International Congress on 3D Materials Science (3DMS 2021)
|
Presentation Title |
Direct Estimation of Structure Parameters from 3D Image Data without Segmentation |
Author(s) |
Elise Otterlei Brenne, Vedrana Andersen Dahl, Peter Stanley Jørgensen |
On-Site Speaker (Planned) |
Elise Otterlei Brenne |
Abstract Scope |
Materials science based on 3D imaging and quantitative analysis is often impeded by the analysis of the image data rather than their acquisition. Human bias and labor-intensive processing of large datasets are of concern. We present a new model for the distribution of intensity and gradient magnitude values in volumetric datasets such as from X-ray computed tomography. In contrast to conventional statistical models such as the Gaussian mixture model, our model accounts for mixed-material interface voxels stemming from blurring inherent to the imaging technique. This results in an improved model fit based on physical parameters the sample and the imaging system. We demonstrate how the model allows for direct extraction of physical sample parameters, like volume fractions and interface areas, without segmenting the data first. The model has potential to improve segmentation accuracy and reproducibility, and with automated maximum-likelihood model parameter estimation, bias from manual segmentation parameter tuning is avoided. |
Proceedings Inclusion? |
Definite: Other |