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X-ray imaging

BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration

V. Nguyen, De Beenhouwer, J., Bazrafkan, S., Hoang, A. - T., Van Wassenbergh, S., and Sijbers, J., “BeadNet: a network for automated spherical marker detection in radiographs for geometry calibration”, in 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020, pp. 518-521.

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Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

T. Van De Looverbosch, Bhuiyan, H. Rahman, Verboven, P., Dierick, M., Van Loo, D., De Beenhouwer, J., Sijbers, J., and Nicolai, B., “Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning”, Food Control, vol. 113, no. 107170, pp. 1-13, 2020.

Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)

B. De Samber, De Rycke, R., De Bruyne, M., Kienhuis, M., Sandblad, L., Bohic, S., Cloetens, P., Urban, C., Polerecky, L., and Vincze, L., “Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS)”, Analytica Chimica Acta, vol. 1106, pp. 22-32, 2020.

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A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants

B. G. Booth, Sijbers, J., and De Beenhouwer, J., “A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants”, Scientific Reports, vol. 10, no. 661, 2020.

Tomography for in-line product inspection

T. De Schryver, Dierick, M., Van Hoorebeke, L., Verboven, P., Van Dael, M., and Sijbers, J., “Tomography for in-line product inspection”, U.S. Patent EP3106863A12015.

An adaptive probability map for the Discrete Algebraic Reconstruction Technique

D. Frenkel, De Beenhouwer, J., and Sijbers, J., “An adaptive probability map for the Discrete Algebraic Reconstruction Technique”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020, pp. 1-6.

Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibers

B. Fröhler, Elberfeld, T., Möller, T., Hege, H. - C., De Beenhouwer, J., Sijbers, J., Kastner, J., and Heinzl, C., “Analysis and Comparison of Algorithms for the Tomographic Reconstruction of Curved Fibers”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.

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Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT

A. Presenti, Bazrafkan, S., Sijbers, J., and De Beenhouwer, J., “Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT”, in 10th Conference on Industrial Computed Tomography (ICT 2020), 2020.

poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT

N. Six, De Beenhouwer, J., and Sijbers, J., “poly-DART: A discrete algebraic reconstruction technique for polychromatic X-ray CT”, Optics Express, vol. 27, no. 23, pp. 33427-33435, 2019.

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A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs

J. Van Houtte, Bazrafkan, S., Vandenberghe, F., Zheng, G., and Sijbers, J., “A Deep Learning Approach to Horse Bone Segmentation from Digitally Reconstructed Radiographs”, in International Conference on Image Processing Theory, Tools, and Applications, 2019.

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