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T. De Kerf, “Hyperspectral imaging for automated inspection of offshore wind infrastructure”, 2023.
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)
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “DTI parameters: Fractional Anisotropy, Radial and Axial Diffusivity reveal seasonal neuroplasticity in the adult songbird brain”, in 4th annual meeting of the Society of Molecular Imaging, Keulen, Germany, 2005.
G. De Groof, Verhoye, M., Leemans, A., Sijbers, J., and Van Der Linden, A., “Paired voxel-wise statistical mapping of in vivo Diffusion Tensor Imaging (DTI) data to assess the seasonal neuronal plasticity in the brain of a songbird”, in 1st Annual Meeting - European Society of Molecular Imaging, Paris, France, 2006.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Using diffusion tensor imaging (DTI) to assess the neuronal plasticity and neuroconnectivity in the brain of a songbird”, in Molecular & Cellular Basis of Neuroconnectivity, Leuven, Belgium, 2006.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Using diffusion tensor imaging (DTI) to assess the neuronal plasticity in the brain of a songbird”, in XXeme Congres du Groupement d'Etudes de Resonance Magnetique, Blankenberge, Belgium, 2006.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Using diffusion tensor imaging (DTI) to assess the neuronal plasticity in the brain of a songbird”, in 4th Annual Symposium – Young Belgian Magnetic Resonance Scientists, Brussels, Belgium, 2005.
G. De Groof, Verhoye, M., Van Meir, V., Tindemans, I., Leemans, A., and Van Der Linden, A., “In vivo diffusion tensor imaging (DTI) of brain subdivisions and vocal pathways in songbirds”, NeuroImage, vol. 29, pp. 754-763, 2006.
G. De Groof, Verhoye, M., Van Meir, V., Tindemans, I., Leemans, A., and Van Der Linden, A., “In Vivo Visualization of the Neuroanatomy and Brain Connectivity of Starling Brain Through Diffusion Tensor Imaging”, in 6th Bi-Annual Meeting – Belgian Society for Neuroscience, Brussels, Belgium, 2005.
G. De Groof, Verhoye, M., Leemans, A., and Van Der Linden, A., “Seasonal changes in neuronal connectivity in the songbird brain discerned by repeated in vivo DTI”, in 13th Scientific Meeting - International Society for Magnetic Resonance in Medicine, Miami, USA, 2005, p. 715.
F. De Carlo, Gursoy, D., Ching, D., Batenburg, K. J., Ludwig, W., Mancini, L., Welford, F. M., Mokso, R., Pelt, D., Sijbers, J., and Rivers, M., “TomoBank: A Tomographic Data Repository for Computational X-ray Science”, Measurement Science and Technology, vol. 29, no. 3, pp. 1-10, 2018.PDF icon Download paper (5.71 MB)
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.
T. De Bondt, Van Hecke, W., Veraart, J., Leemans, A., Sijbers, J., Sunaert, S., Jacquemyn, Y., and Parizel, P. M., “Does the use of hormonal contraceptives cause microstructural changes in cerebral white matter? Preliminary results of a DTI and tractography study.”, European radiology, vol. 23, no. 1, pp. 57-64, 2013.PDF icon Download paper (411.18 KB)
B. De Boeck, Scheunders, P., and Van Dyck, D., “From inductive inference to the fundamental equations of measuerement”, in Proc. First International Conference on Complex Systems, 2000, pp. 115-122.
B. De Boeck, “Infotree - A study of its foundations”, University of Antwerp, Antwerp, 2001.
J. De Beenhouwer, Palenstijn, W. J., Bleichrodt, F., Batenburg, K. J., and Sijbers, J., “A framework for markerless alignment with full 3D flexibility”, European Microscopy Conference. 2012.PDF icon Download abstract (117.9 KB)
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., “Chapter One - Introduction”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
S. De Backer, Cornelissen, F., Lemeire, J., Nuydens, R., Meert, T., Schelkens, P., and Scheunders, P., “Mosiacing of Fibered Fluorescence Microscopy Video”, Lecture notes in Computer Science, vol. 5259, pp. 915-923, 2008.
S. De Backer, Kempeneers, P., Debruyn, W., and Scheunders, P., “A Band Selection Technique for Spectral Classification”, IEEE Geoscience and Remote Sensing Letters, vol. 2, pp. 319-323, 2005.
S. De Backer and Scheunders, P., “Enhancement of fMRI image series with the aid of an anatomical image”, in ISBI’06, IEEE International Symposium on Biomedical Imaging, Arlington, Virginia, april 6-9, 2006, pp. 1052-1055.
S. De Backer and Scheunders, P., “A competitive elliptical clustering algorithm”, Pattern Recognition Letters, vol. 20, pp. 1141-1147, 1999.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., “Chapter Five - Optimal experiment design for nanoparticle atom counting from ADF STEM images”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
S. De Backer, “Unsupervised Pattern Recognition - Dimensionality Reduction and Classification”, University of Antwerp, Antwerp, 2002.
S. De Backer and Scheunders, P., “Texture segmentation by frequency-sensitive elliptical competitive learning”, in Proc. ICIAP99, International Conference on Image Analysis and Processing , Venice, Italy, september 27-29, 1999, pp. 64-69.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., “Chapter Two - Statistical parameter estimation theory: principles and simulation studies”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.

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