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Conference Paper
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Aerts, P., Dirckx, J. J. J., and Sijbers, J., “A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system”, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.93 MB)
J. Sijbers and den Dekker, A. J., “Mapping a polyhedron onto a sphere: application to Fourier descriptors”, in 22nd Benelux Meeting on Systems and Control, Lommel, Belgium, 2003.
J. Rajan, Verhoye, M., and Sijbers, J., “A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood”, in SPIE Medical Imaging, 2011, vol. 7962.
A. J. den Dekker and Sijbers, J., “Maximum Likelihood estimation of signal amplitude and noise variance”, in 13th IFAC Symposium on System Identification (SYSID-2003), Rotterdam, The Netherlands, 2003, pp. 126-131.
A. J. den Dekker, Sijbers, J., Verhoye, M., and Van Dyck, D., “Maximum Likelihood estimation of the signal component magnitude in phase contrast MR images”, in Proceedings of SPIE Medical Imaging, San Diego, California, USA, 1998, vol. 3338, pp. 408-415.
W. Van Aarle, Ghysels, P., Sijbers, J., and Vanroose, W., “Memory access optimization for iterative tomography on many-core architectures”, in The 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013, pp. 364-367.PDF icon fully3d_wvaarle.pdf (2.8 MB)
T. Huysmans and Sijbers, J., “Mesh smoothing through multiscale anisotropic diffusion of geometry images”, in 13th European Microscopy Congress (EMC), Antwerp, Belgium, 2004.PDF icon Full text (414.55 KB)
J. Renders, De Beenhouwer, J., and Sijbers, J., “Mesh-based reconstruction of dynamic foam images using X-ray CT”, in International Conference on 3D Vision (3DV2021), 2021, pp. 1312-1320.
A. Leemans, Van Hecke, W., Lebel, C., Walker, L., Sijbers, J., and Beaulieu, C., “A model based approach for voxelwise analysis of multi-subject diffusion tensor data”, in Joint Annual Meeting ISMRM-ESMRMB, Berlin, 2007.
E. Van de Casteele, Van Dyck, D., Sijbers, J., and Raman, E., “A model-based correction method for beam-hardening artifacts in X-ray tomography”, in 22nd Benelux Meeting on Systems and Control, Lommel, Belgium, 2003.
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., “Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging”, in 8th WHISPERS - Evolution in Remote Sensing, Los Angeles, USA, 2016.
E. Van de Casteele, Van Dyck, D., Sijbers, J., and Raman, E., “Modelling of Beam hardening in micro CT”, in IEEE International Symposium on Biomedical Imaging, Washington DC, USA, 2003, pp. 57-58.PDF icon Download full paper (163.52 KB)
A. Bernat, Huysmans, T., Van Glabbeek, F., Sijbers, J., Bortier, H., and Gielen, J. L., “Morphometric Study of the Human Clavicle for the Development of an Anatomical Plate”, in 54th Annual Meeting of the Orthopaedic Research Society, San Francisco, Ca, United States, 2008.PDF icon Full text (578.18 KB)
C. C. Sabino, Andrade, L. S., Tsang, I. R., Cavalcanti, G. D. C., Tsang, I. J., and Sijbers, J., “Motion Compensation Techniques in Permutation-Based Video Encryption”, in Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Washington, DC, USA, 2013, pp. 1578–1581.
S. Scataglini, Danckaers, F., Haelterman, R., Huysmans, T., and Sijbers, J., “Moving Statistical Body Shape Models Using Blender”, in Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), Cham, 2019, pp. 28–38.
V. Andrejchenko, Heylen, R., Liao, W., Philips, W., and Scheunders, P., “MRF-based decision fusion for hyperspectral image classification”, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018.
R. F. Kooy, Reyniers, E., Verhoye, M., Sijbers, J., Fransen, E., Van Camp, C., Cras, P., Oostra, B. A., Van Der Linden, A., and Willems, P. J., “MRI as a tool to study brain structure from mouse models of mental retardation”, in Abstracts of the 8th international workshop on Fragile X syndrome and X-linked mental retardation, Picton, Ontario, Canada, 1997.
M. Verhoye, Sijbers, J., Kooy, R. F., Reyniers, E., Fransen, E., Oostra, B. A., Willems, P. J., and Van Der Linden, A., “MRI as a tool to study brain structure from mouse models of mental retardation”, in Proceedings of SPIE Medical Imaging, San Diego, CA, USA, 1998, vol. 3337, pp. 314-320.
W. Van Hecke, Leemans, A., De Backer, S., Vandervliet, E., Parizel, P. M., D'Agostino, E., and Sijbers, J., “Multi-channel coregistration of diffusion tensor images based on a viscous fluid model”, in Belgian Day on Biomedical Engineering - IEEE/EMBS Benelux Symposium, Brussels, Belgium, 2006, pp. 139-142.
B. Shafieizargar, Jeurissen, B., Poot, D. H. J., den Dekker, A. J., and Sijbers, J., “Multi-contrast multi-shot EPI for accelerated diffusion MRI”, in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021, pp. 3869-3872.
R. Heylen and Scheunders, P., “Multi-dimensional Pixel Purity Index”, in IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013.
T. Huysmans, Danckaers, F., Vleugels, J., Lacko, D., De Bruyne, G., Verwulgen, S., and Sijbers, J., “Multi-patch B-Spline Statistical Shape Models for CAD-Compatible Digital Human Modeling”, in Advances in Human Factors in Simulation and Modeling, Cham, 2019, pp. 179–189.
P. - J. Vanthienen, Francken, N., Sijbers, J., and De Beenhouwer, J., “Multi-resolution gratings for edge illumination x-ray phase contrast imaging: a simulation study”, in Developments in X-Ray Tomography XV, 2024, vol. 13152, p. 1315208.
A. Pizurica, Scheunders, P., and Philips, W., “Multiresolution multispectral image denoising based on probability of presence of features of interest”, in Advanced Concepts in Image and Vision Systems, Brussels, 2004, pp. 357-364.
A. Leemans and Sijbers, J., “Multiresolutional rigid-body registration for space curves”, in IEEE Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, 2004, pp. 215-221.

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