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Journal of Imaging, Volume 7, Issue 1

2021 January - 11 articles

Cover Story: An unsupervised machine learning technique is presented that is reinforced with hypothesis testing and statistical inference to iteratively segment the reconstructed image of a breast into fat, transition, fibroglandular, and malignant tissues. This segmentation leads to decomposition of the breast interior into disjoint tissue masks. An array of metrics is applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed, and are supplemented with qualitative information. View this paper

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1 Citations

3,628 Views

12 Pages

Comparison of Thermal Neutron and Hard X-ray Dark-Field Tomography

Alex Gustschin,
Tobias Neuwirth,
Alexander Backs,
Manuel Viermetz,
Nikolai Gustschin,
Michael Schulz and
Franz Pfeiffer

J. Imaging2021, 7(1), 1;https://doi.org/10.3390/jimaging7010001

23 December 2020

High visibility (0.56) neutron-based multi-modal imaging with a Talbot–Lau interferometer at a wavelength of $1.6$ Å is reported. A tomography scan of a strongly absorbing quartz geode sample was performed with both the neutron and an X-ray...

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J. Imaging - ISSN 2313-433X

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