Publications

Export 1340 results:
Author [ Type(Desc)] Year
Journal Article
T. Roelandts, Batenburg, K. J., den Dekker, A. J., and Sijbers, J., “The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography”, Computer Vision and Image Understanding, vol. 126, pp. 28-37, 2014.PDF icon Download paper (4.58 MB)
G. Van Gompel, Defrise, M., and Batenburg, K. J., “Reconstruction of a uniform star object from interior x-ray data: uniqueness, stability and algorithm”, Inverse Problems, vol. 25, 2009.
B. Auer, Kalluri, K., De Beenhouwer, J., Doty, K., Zeraatkar, N., Kuo, P. H., Furenlid, L. R., and King, M. A., “Reconstruction using Depth of Interaction Information of Curved and Flat Detector Designs for Quantitative Multi-Pinhole Brain SPECT”, Journal of Nuclear Medicine, vol. 61, p. 103, 2020.PDF icon snm2020_recon_curved.pdf (103.23 KB)
E. Ribeiro Sabidussi, Klein, S., Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., “Recurrent Inference Machines as inverse problem solvers for MR relaxometry”, Medical Image Analysis, vol. 74, pp. 1-11, 2021.PDF icon Download paper (2.26 MB)
C. M. W. Tax, Jeurissen, B., Vos, S. B., Viergever, M. A., and Leemans, A., “Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.”, NeuroImage, vol. 86, pp. 67-80, 2014.
J. Sijbers, Van Audekerke, J., Verhoye, M., Van Der Linden, A., and Van Dyck, D., “Reduction of ECG and gradient related artifacts in simultaneously recorded human EEG/MRI data”, Magnetic Resonance Imaging, vol. 18, pp. 881-886, 2000.PDF icon Download paper (2 MB)
J. Sijbers and Postnov, A., “Reduction of ring artifacts in high resolution micro-CT reconstructions”, Physics in Medicine and Biology, vol. 49, pp. 247-253, 2004.
U. Roine, Ripatti, S., Rehnström, K., Roine, T., Kilpinen, H., Surakka, I., Wedenoja, J., Ylisaukko-oja, T., Kempas, E., Wessman, J., Moilanen, I., Mattila, M. - L., Kielinen, M., Jussila, K., Suomalainen, S., Pulkkinen, E., von Wendt, L., and Peltonen, L., “Reelin Associated With Restricted and Stereotyped Behavior Based on Principal Component Analysis on Autism Diagnostic Interview-Revised”, Autism - Open Access, vol. 3, no. 1, 2013.
A. Cuyt, Sijbers, J., Verdonk, B., and Van Dyck, D., “Region and Contour Identification of Physical Objects”, Applied Numerical Analysis Computational Mathematics, vol. 1, pp. 343-352, 2004.
T. De Bondt, Jacquemyn, Y., Van Hecke, W., Sijbers, J., Sunaert, S., and Parizel, P. M., “Regional gray matter volume differences and sex-hormone correlations as a function of menstrual cycle phase and hormonal contraceptives use.”, Brain research, vol. 1530, pp. 22-31, 2013.
G. Van Eyndhoven, Batenburg, K. J., and Sijbers, J., “Region-based iterative reconstruction of structurally changing objects in CT”, IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 909-919, 2014.
T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Philips, W., Sijbers, J., and Leemans, A., “Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks”, Medical Image Analysis, vol. 52, pp. 56-67, 2019.PDF icon Download paper (4.03 MB)
A. J. den Dekker and van den Bos, A., “Resolution: a survey”, J. Opt. Soc. Am. A, vol. 14, pp. 547–557, 1997.
J. Sijbers, Michiels, I., Verhoye, M., Van Audekerke, J., Van Der Linden, A., and Van Dyck, D., “Restoration of MR induced artifacts in simultaneously recorded MR/EEG data”, Magnetic Resonance Imaging, vol. 17, pp. 1383-1393, 1999.PDF icon Download paper (1.09 MB)
Z. Mai, Rajan, J., Verhoye, M., and Sijbers, J., “Robust edge-directed interpolation of magnetic resonance images”, Physics in medicine and biology, vol. 56, pp. 7287-7303, 2011.PDF icon Download paper (1.3 MB)
B. Koirala, Zahiri, Z., and Scheunders, P., “A Robust Supervised Method for Estimating Soil Moisture Content From Spectral Reflectance”, IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1-13, 2022.PDF icon soil_moisture_content.pdf (7.95 MB)
B. Koirala, Zahiri, Z., Lamberti, A., and Scheunders, P., “Robust supervised method for nonlinear spectral unmixing accounting for endmember variability”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7434-7448, 2021.PDF icon ieee_version.pdf (3.76 MB)
F. Calamante, Jeurissen, B., Smith, R. E., Tournier, J. - D., and Connelly, A., “The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density”, Magnetic Resonance in Medicine, vol. 79, no. 5, pp. 2738–2744, 2018.
P. Pullens, Bladt, P., Sijbers, J., Maas, A. I. R., and Parizel, P. M., “A safe, cheap and easy-to-use isotropic diffusion phantom for clinical and multicenter studies”, Medical Physics, vol. 44, no. 3, pp. 1063–1070, 2017.
I. J. Tsang, Tsang, I. R., De Boeck, B., and Van Dyck, D., “Scaling and critical probability for cluster size and lattice animals diversity on randomly occupied square lattices”, Journal of Physics A: Mathematical and General, vol. 33, pp. 2739-2754, 2000.
M. Van Dael, Lebotsa, S., Herremans, E., Verboven, P., Sijbers, J., Opara, U. L., Cronje, U. L., and Nicolai, B., “A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs”, Postharvest Biology and Technology, vol. 112, pp. 205-214, 2016.
N. Chabi, Iuso, D., Beuing, O., Preim, B., and Saalfeld, S., “Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.”, Int J Comput Assist Radiol Surg, 2022.
K. J. Batenburg, Van Aarle, W., and Sijbers, J., “A Semi-Automatic Algorithm for Grey Level Estimation in Tomography”, Pattern Recognition Letters, vol. 32, pp. 1395-1405, 2011.
W. Liao, Pizurica, A., Scheunders, P., Philips, W., and Pi, Y., “Semi-supervised local discriminant analysis for feature extraction in hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 184-198, 2013.
G. Zhang, Scheunders, P., Cerra, D., and Muller, R., “Shadow-aware nonlinear spectral unmixing for hyperspectral imagery”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5514-5533, 2022.PDF icon shadow-aware_nonlinear_spectral_unmixing_for_hyperspectral_imagery.pdf (9.51 MB)

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