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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, 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, 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 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., Texture segmentation by frequency-sensitive elliptical competitive learning, Image and Vision Computing, vol. 19, pp. 639-648, 2001.
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, Unsupervised Pattern Recognition - Dimensionality Reduction and Classification, University of Antwerp, Antwerp, 2002.
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., 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.
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)
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.
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)
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., 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.
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.
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., 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.
B. De Samber, De Rycke, R., De Bruyne, M., Kienhuis, M., Sandblad, L., Bohic, S., Cloetens, P., Urban, C., Polerecky, L., and Vincze, L., Effect of sample preparation techniques upon single cell chemical imaging: A practical comparison between synchrotron radiation based X-ray fluorescence (SR-XRF) and Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS), Analytica Chimica Acta, vol. 1106, pp. 22-32, 2020.PDF icon Download paper (4.01 MB)

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