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Journal Article
M. Siqueira Pinto, Paolella, R., Billiet, T., Van Dyck, P., Guns, P. - J., Jeurissen, B., Ribbens, A., den Dekker, A. J., and Sijbers, J., “Harmonisation of Brain Diffusion MRI: Concepts and Methods”, Frontiers in Neuroscience , vol. 14, pp. 1-17, 2020.PDF icon Download paper (2.61 MB)
M. Mohammadian, Roine, T., Hirvonen, J., Kurki, T., Ala-Seppälä, H., Frantzén, J., Katila, A., Kyllönen, A., Maanpää, H. - R., Posti, J., Takala, R., Tallus, J., and Tenovuo, O., “High angular resolution diffusion-weighted imaging in mild traumatic brain injury”, NeuroImage: Clinical, vol. 13, pp. 174 - 180, 2017.PDF icon Download paper (848.3 KB)
W. Keustermans, Huysmans, T., Danckaers, F., Zarowski, A., Schmelzer, B., Sijbers, J., and Dirckx, J. J. J., “High quality statistical shape modelling of the human nasal cavity and applications”, Royal Society Open Science, vol. 5, no. 12, 2018.PDF icon Download paper (1.85 MB)
P. Scheunders and De Backer, S., “High-dimensional clustering using frequency sensitive competitive learning”, Pattern Recognition, vol. 32, pp. 193-202, 1999.
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020.PDF icon remotesensing-12-01728.pdf (2.27 MB)
A. J. den Dekker, Sijbers, J., and Van Dyck, D., “How to optimize the design of a quantitative HREM experiment so as to attain the highest precision”, Journal of Microscopy, vol. 194, pp. 95-104, 1999.PDF icon Download full paper (601.48 KB)
M. Zare, M. Helfroush, S., Kazemi, K., and Scheunders, P., “Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition”, Remote Sensing, vol. 13, no. 2930, 2021.PDF icon remotesensing-13-02930.pdf (3.79 MB)
A. Karami, Heylen, R., and Scheunders, P., “Hyperspectral Image Compression Optimized for Spectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. pp, no. 99, 2016.
T. Hu, Li, W., Liu, N., Tao, R., Zhang, F., and Scheunders, P., “Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 2, pp. 1516-1533, 2021.PDF icon tgrs_2020.pdf (5.71 MB)
R. Heylen and Scheunders, P., “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio's”, IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 570-579, 2013.
M. Brackx, Van Wittenberghe, S., Verhelst, J., Scheunders, P., and Samson, R., “Hyperspectral leaf reflectance of Carpines betulus L. saplings for urban air quality estimation”, Environmental Pollution, vol. 220, pp. 159-167, 2017.
J. Bioucas-Dias, Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., and Chanussot, J., “Hyperspectral remote sensing data analysis and future challenges”, IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 2, pp. 6-36, 2013.
P. Ghosh, Roy, S. Kumar, Koirala, B., Rasti, B., and Scheunders, P., “Hyperspectral Unmixing Using Transformer Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022.PDF icon transformer_unmixing.pdf (6.32 MB)
P. Gosh, Roy, S. Kumar, Koirala, B., Rasti, B., and Scheunders, P., “Hyperspectral Unmixing using Transformer Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022.PDF icon transformer_unmixing_author_version_to_upload.pdf (14.65 MB)
R. Heylen, Zare, A., Gader, P., and Scheunders, P., “Hyperspectral unmixing with endmember variability via alternating angle minimization”, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4983-4993, 2016.
I. Blockx, Verhoye, M., Van Audekerke, J., Bergwerf, I., Kane, J. X., Delgado Y Palacios, R., Veraart, J., Jeurissen, B., Raber, K., Von Hörsten, S., Ponsaerts, P., Sijbers, J., Leergaard, T. B., and Van Der Linden, A., “Identification and characterization of Huntington related pathology: an in vivo DKI imaging study”, NeuroImage, vol. 63, no. 2, pp. 653-662, 2012.
T. De Kerf, Pipintakos, G., Zahiri, Z., Vanlanduit, S., and Scheunders, P., “Identification of corrosion minerals usning shortwave infrared hyperspectral imaging”, Sensors, vol. 22, no. 1, p. 407, 2022.PDF icon sensors-22-00407-v2.pdf (4.41 MB)
I. J. Tsang, Tsang, I. R., and Van Dyck, D., “Image processing using neighbourhood coding”, Pattern Recognition Letters, vol. 20, pp. 1279-1286, 1999.
J. Deng, Renders, J., Meenaketan, B. Linkoon P., Takalloo, S. Ebrahimi, Braeken, D., De Beenhouwer, J., and Sijbers, J., “Imaging Algorithm and Optimal Measurement Protocol for 3D Impedance Tomography on 2D High Density Micro Electrode Array”, IEEE Sensors, vol. 24, no. 15, pp. 24452-24465, 2024.
M. Verhoye, Van Der Linden, A., Van Audekerke, J., Sijbers, J., Eens, M., and Balthazart, J., “Imaging birds in a bird cage: in-vivo FSE 3D MRI of bird brain”, MAGMA: Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 6, pp. 22-27, 1998.PDF icon Download paper (1.33 MB)
A. J. den Dekker and Sijbers, J., “Implications of the Rician distribution for fMRI generalized likelihood ratio tests”, Magnetic Resonance Imaging, vol. 23, pp. 953-959, 2005.PDF icon Download paper (1.36 MB)
E. Byl, Bladt, P., Lebeer, S., and Kiekens, F., “Importance of pressure plasticity during compression of probiotic tablet formulations”, European Journal of Pharmaceutics and Biopharmaceutics, vol. 145, pp. 7-11, 2019.
D. H. J. Poot, Pintjens, W., Verhoye, M., Van Der Linden, A., and Sijbers, J., “Improved B0 field map estimation for high field EPI”, Magnetic Resonance Imaging, vol. 28, pp. 441-450, 2010.PDF icon Download paper (1.29 MB)
V. Anania, Collier, Q., Veraart, J., Buikema, A. Eline, Vanhevel, F., Billiet, T., Jeurissen, B., den Dekker, A. J., and Sijbers, J., “Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model”, NeuroImage, vol. 256, p. 119219, 2022.
Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography.”, PloS one, vol. 7, no. 8, p. e44074, 2012.

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