Publications

Export 1324 results:
Author [ Type(Asc)] Year
Journal Article
D. H. J. Poot, den Dekker, A. J., Achten, E., Verhoye, M., and Sijbers, J., “Optimal experimental design for Diffusion Kurtosis Imaging”, IEEE Transactions on Medical Imaging, vol. 29, pp. 819-829, 2010.PDF icon Download paper (1.12 MB)
J. Morez, Szczepankiewicz, F., den Dekker, A. J., Vanhevel, F., Sijbers, J., and Jeurissen, B., “Optimal experimental design and estimation for q-space trajectory imaging”, Human Brain Mapping, vol. 44, no. 4, pp. 1793-1809, 2023.PDF icon Download paper (5.87 MB)
M. Van Dael, Rogge, S., Verboven, P., Saeys, W., Sijbers, J., and Nicolai, B., “Online Tomato Inspection Using X-Ray Radiographies and 3- Dimensional Shape Models”, Chemical Engineering Transactions, vol. 44, pp. 37-42, 2015.
M. Baillieux and Scheunders, P., “On-line determination of the velocity of simultaneously moving organisms by image analysis for the detection of sublethal toxicity”, Water Research, vol. 32, pp. 1027-1034, 1998.
G. Ramos-Llordén, Vegas-Sánchez-Ferrero, G., Björk, M., Vanhevel, F., Parizel, P. M., Estépar, R. San José, den Dekker, A. J., and Sijbers, J., “NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping”, IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2414 - 2427, 2018.PDF icon Download paper (3.3 MB)
M. Naeyaert, Roose, D., Mai, Z., Keliris, A. J., Sijbers, J., Van Der Linden, A., and Verhoye, M., “Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content”, Journal of Magnetic Resonance, vol. 300, pp. 18-27, 2019.
W. Van Hecke, Leemans, A., D'Agostino, E., De Backer, S., Vandervliet, E., Parizel, P. M., and Sijbers, J., “Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information”, IEEE Transactions on Medical Imaging, vol. 26, pp. 1598-1612, 2007.PDF icon Download paper (1.85 MB)
P. V. Sudeep, Palanisamy, P., Kesavadas, C., Sijbers, J., den Dekker, A. J., and Rajan, J., “A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps”, Signal Image and Video Processing, vol. 11, no. 5, pp. 913-920, 2017.
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images”, Magnetic Resonance Imaging, vol. 30, no. 10, pp. 1512-1518, 2012.PDF icon Download full paper (1.11 MB)
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.”, Magnetic resonance imaging, vol. 30, no. 10, pp. 1512-8, 2012.
R. Heylen, Scheunders, P., Gader, P., and Rangarajan, A., “Nonlinear unmixing by using different metrics in a linear unmixing chain”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015.
B. Koirala, Rasti, B., Bnoulkacem, Z., and Scheunders, P., “Nonlinear Spectral Unmixing Using Bézier Surfaces”, IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024.PDF icon Download paper (9.66 MB)
R. Heylen, Burazerovic, D., and Scheunders, P., “Nonlinear spectral unmixing by geodesic simplex volume maximization”, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 534-542, 2011.
P. Scheunders, De Backer, S., and Naud, A., “Non-linear mapping for feature extraction”, Lecture Notes in Computer Science, vol. 1451, pp. 823-830, 1998.
R. Heylen, Andrejchenko, V., Zahiri, Z., Parente, M., and Scheunders, P., “Nonlinear hyperspectral unmixing with graphical models”, IEEE Transaction on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4844-4856, 2019.PDF icon published.pdf (3.15 MB)
S. De Backer, Naud, A., and Scheunders, P., “Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction”, Pattern Recognition Letters, vol. 19, pp. 711-720, 1998.
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.
T. Van De Looverbosch, Raeymaekers, E., Verboven, P., Sijbers, J., and Nicolai, B., “Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning”, Expert Systems with Applications, vol. 176, no. 114925, pp. 1-12, 2021.
M. Shahrimie Asaari, Mertens, S., Verbraeken, L., Dhondt, S., Inze, D. G., Koirala, B., and Scheunders, P., “Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress”, Computers and Electronics in Agriculture, vol. 195, 2022.
M. Shahrimie Asaari, Mertens, S., Verbraeken, L., Dhondt, S., Inze, D., Koirala, B., and Scheunders, P., “Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress”, Computers and Electronics in Agriculture, vol. 195, no. 106806, 2022.PDF icon 1-s2.0-s0168169922001235-main.pdf (5.53 MB)
Y. Zhang, De Backer, S., and Scheunders, P., “Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3834-3843, 2009.
A. Karami, M.Yazdi,, and Zolghadre, A., “Noise Reduction of Hyperspectral Images Using Kernel Nonnegative Tucker Decomposition”, IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 3, 2011.
B. Rasti, Scheunders, P., Ghesami, P., Licciardi, G., and Chanussot, J., “Noise reduction in hyperspectral imagery: overview and application”, Remote Sensing , vol. 10, no. 3, p. 482, 2018.
J. Rajan, Poot, D. H. J., Juntu, J., and Sijbers, J., “Noise measurement from magnitude MRI using local estimates of variance and skewness.”, Physics in medicine and biology, vol. 55, no. 16, pp. N441-9, 2010.PDF icon Download paper (219.85 KB)
J. Rajan, den Dekker, A. J., and Sijbers, J., “A new non local maximum likelihood estimation method for Rician noise reduction in Magnetic Resonance images using the Kolmogorov-Smirnov test”, Signal Processing, vol. 103, pp. 16-23, 2014.

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