Publications

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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 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.
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, “Infotree - A study of its foundations”, University of Antwerp, Antwerp, 2001.
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.
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)
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.
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., “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, Naud, A., and Scheunders, P., “Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction”, Pattern Recognition Letters, vol. 19, pp. 711-720, 1998.
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 and Scheunders, P., “Texture segmentation by frequency-sensitive elliptical competitive learning”, Image and Vision Computing, vol. 19, pp. 639-648, 2001.
S. De Backer, “Unsupervised Pattern Recognition - Dimensionality Reduction and Classification”, University of Antwerp, Antwerp, 2002.
S. De Backer, Pizurica, A., Huysmans, B., Philips, W., and Scheunders, P., “Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators”, Image and Vision Computing, vol. 26, pp. 1038-1051, 2008.
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.
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, 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.
A. De Backer, Fatermans, J., den Dekker, A. J., and Van Aert, S., “Chapter Three - Efficient fitting algorithm”, in Advances in Imaging and Electron Physics, vol. 217, Science Direct Elsevier, 2021.

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