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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 Eight - General conclusions and future perspectives”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.
A. De Backer, van den Bos, K. H. W., Van den Broek, W., Sijbers, J., and Van Aert, S., “StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images”, Ultramicroscopy, vol. 171, pp. 104–116, 2016.
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.
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 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.
S. De Backer, Kempeneers, P., Debruyn, W., and Scheunders, P., “Wavelet Based Hyperspectral Data Analysis for Vegetation Stress Classification”, in Proc. of Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, 2004, pp. 387-391.
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.
S. De Backer, “Unsupervised Pattern Recognition - Dimensionality Reduction and Classification”, University of Antwerp, Antwerp, 2002.
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, Kempeneers, P., Debruyn, W., and Scheunders, P., “Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images”, in Image Analysis and Recognition Proc. of International Conference on Image Analysis and Recognition, Porto, Portugal, 2004, pp. 497-503.
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.
S. De Backer and Scheunders, P., “Texture segmentation by frequency-sensitive elliptical competitive learning”, Image and Vision Computing, vol. 19, pp. 639-648, 2001.
J. De Beenhouwer, Sijbers, J., and Vanthienen, P. - J., “Mask assembly for edge illumination phase contrast radiation imaging”, U.S. Patent EP2025062978W2024.PDF icon mask_patent.pdf (6.93 MB)
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)
J. De Beenhouwer and Sijbers, J., “A PHASE‑CONTRAST X‑RAY IMAGING SYSTEM FOR OBTAINING A DARK‑FIELD IMAGE AND A METHOD THEREFOR”, U.S. Patent EP 4 351 425 B12025.PDF icon Download patent (547.58 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.
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)
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.
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)
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., “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.

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