Publications

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H. Struyfs, Van Hecke, W., Veraart, J., Sijbers, J., Slaets, S., De Belder, M., Wuyts, L., Peters, B., Sleegers, K., Robberecht, C., Van Broeckhoven, C., De Belder, F., Parizel, P. M., and Engelborghs, S., “Diffusion Kurtosis Imaging: a possible MRI biomarker for AD diagnosis?”, Journal of Alzheimer’s Disease, vol. 48, pp. 937-948, 2015.PDF icon Download paper (374.95 KB)
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.
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F. Tajdari, Huysmans, T., Yang, Y., and Song, Y., “Feature preserving non-rigid iterative weighted closest point and semi-curvature registration”, IEEE Transactions on Image Processing, 2022.
N. Tanmay, Liu, G., Hassan, B., Weyn, B., De Backer, S., and Scheunders, P., “Automated Social behaviour Recognition At Low Resolution”, in ICPR14, International Conference on Pattern Recognition, Stockholm, Sweden, 2014.
X. Tao, Koirala, B., Plaza, A., and Scheunders, P., “A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-1, 2024.PDF icon Download paper (2.13 MB)
A. -yhuwertMurcia Tapias, Giraldo, D., and Romero, E., “Synthesizing fractional anisotropy maps from T1-weighted magnetic resonance images using a simplified generative adversarial network”, in Medical Imaging 2024: Clinical and Biomedical Imaging, 2024, vol. 12930, p. 129302P.
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 diffusion ODF sharpening”, ISMRM Workshop on Diffusion as a Probe of Neural Tissue Microstructure. Podstrana, Croatia, 2013.
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.
C. M. W. Tax, Jeurissen, B., Viergever, M. A., and Leemans, A., “Robust fiber response function estimation for deconvolution based diffusion MRI methods”, International Society for Magnetic Resonance in Medicine, vol. 21. Salt Lake City, Utah, p. 3149, 2013.
C. M. W. Tax, Jeurissen, B., Viergever, M. A., and Leemans, A., “Robust fiber response function estimation for deconvolution based diffusion MRI methods”, ISMRM Benelux Chapter, vol. 5. Rotterdam, The Netherlands, p. 55, 2013.
G. Thoonen, Nys, B., Haegen, V. Y., Roy, D. G., and Scheunders, P., “Automatic forensic analysis of automotive paints using optical microscopy”, Forensic Science International, vol. 259, pp. 210-220, 2016.
G. Thoonen, Mahmood, Z., Peeters, S., and Scheunders, P., “Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion”, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 5, pp. 510 - 521, 2012.
G. Thoonen, Vanden Borre, J., De Backer, S., and Scheunders, P., “Assessing the quality of heathland vegetation by classification of hyperspectral data using spatial information”, in Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009, Cape Town, South Africa, 2009, vol. 4, p. IV-330 - IV-333.
G. Thoonen, “Contextual classification of hyperspectral remote sensing images - Application in vegetation monitoring”, University of Antwerp, Antwerp, Belgium, 2012.PDF icon Download thesis (17.15 MB)
G. Thoonen, Hufkens, K., Vanden Borre, J., Spanhove, T., and Scheunders, P., “Accuracy assessment of contextual classification results for vegetation mapping”, International Journal of Applied Earth Observation and Geoinformation, vol. 15, pp. 7 - 15, 2012.
G. Thoonen, Hufkens, K., Vanden Borre, J., and Scheunders, P., “Using patch metrics as validation for contextual classification of heathland vegetation.”, in Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference, 2010, vol. XXXVIII-4/C7.
G. Thoonen, Spanhove, T., Vanden Borre, J., and Scheunders, P., “Classification of heathland vegetation in a hierarchical contextual framework”, International Journal of Remote Sensing, vol. 34, no. 1, pp. 96 - 111, 2013.
G. Thoonen, De Backer, S., Provoost, S., Kempeneers, P., and Scheunders, P., “Spatial Classification of Hyperspectral Data of Dune Vegetation along the Belgian Coast”, in Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, Boston, MA, USA, 2008, vol. 3, p. III-483 - III-486.
G. Thoonen, Spanhove, T., Haest, B., Vanden Borre, J., and Scheunders, P., “Habitat mapping and quality assessment of heathlands using a modified kernel-based reclassification technique”, in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, Honolulu, HI, USA, 2010, pp. 2707 - 2710.
G. Tisson, Scheunders, P., and Van Dyck, D., “3D region of interest X-ray CT for geometric magnification from multiresolution acquisitions”, in Proc. ISBI04, IEEE International Symposium on Biomedical Imaging, 15-18 april 2004, Arlington, VA, 2004, pp. 567-570.
G. Tisson, Scheunders, P., and Van Dyck, D., “ROI Cone-Beam CT on a Circular Orbit for Geometric Magnification Using Reprojection”, in Proc. IEEE Medical Imaging Conference, Rome, 16-22 october, 2004.
G. Tisson, “Reconstructie van Transversaal Getrunceerde Cone Beam Projecties in Micro-Tomografie”, University of Antwerp, Antwerp, 2006.
L. Tits, Somers, B., Heylen, R., Scheunders, P., and Coppin, P., “Improving the efficiency of MESMA through geometric unmixing principles”, in SPIE Remote Sensing, Drseden, Germany, 23-26 September , 2013, vol. 8892.
L. Tits, Heylen, R., Somers, B., Scheunders, P., and Coppin, P., “A geometric unmixing concept for the selection of optimal binary endmember combinations”, IEEE Geoscience and Remote Sensing letters, vol. 12, pp. 82-86, 2015.
J. - D. Tournier, Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C. - H., and Connelly, A., “MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.”, Neuroimage, p. 116137, 2019.

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