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D
A. J. den Dekker and Sijbers, J., “Advanced Image Processing in Magnetic Resonance Imaging”, in Series: Signal Processing and Communications, vol. 27, L. Landini, Ed. Marcel Dekker, 2005, pp. 85-143.
A. J. den Dekker, Poot, D. H. J., Bos, R., and Sijbers, J., “Likelihood based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a simulation study”, IEEE Transactions on Medical Imaging, vol. 28, pp. 287-296, 2009.
J. Deng, Renders, J., Meenaketan, B. Linkoon P., Takalloo, S. Ebrahimi, Braeken, D., De Beenhouwer, J., and Sijbers, J., “Imaging Algorithm and Optimal Measurement Protocol for 3D Impedance Tomography on 2D High Density Micro Electrode Array”, IEEE Sensors, vol. 24, no. 15, pp. 24452-24465, 2024.
T. Dhollander, Smith, R. E., Tournier, J. - D., Jeurissen, B., and Connelly, A., “Time to Move On: An FOD-Based DEC Map to Replace DTI's Trademark DEC FA”, ISMRM 23th Annual Meeting. p. 1027, 2015.
T. Dhollander, Veraart, J., Van Hecke, W., Maes, F., Sunaert, S., Sijbers, J., and Suetens, P., “Feasibility and advantages of diffusion weighted imaging atlas construction in Q-space”, in MICCAI 2011: Medical Image Computing and Computer-Assisted Intervention, 2011, pp. 166-173.
E. Dhondt, Jeurissen, B., Danneels, L., and Van Oosterwijck, J., “Structural adaptations of cognitive emotional brain regions are linked to endogenous pain modulation: a psychophysical and brain imaging study in healthy people and in low back pain”, 10th Interdisciplinary World Congress on Low Back and Pelvic Girdle Pain. p. 399, 2019.
E. Dhondt, Jeurissen, B., Danneels, L., and Van Oosterwijck, J., “Structural alterations of cognitive emotional brain regions are linked to the presence of spinal sensitization in low back pain”, 9th Biennial Congress of the Belgian Back Society, entitled ``The Challenge of activating Patients with Low Back Pain``. 2018.
C. Distante, Blanc-Talon, J., Philips, W., Popescu, D., and Scheunders, P., “ACIVS 2016, Advanced Concepts for Intelligent Vision Systems”, Lecture Notes in Computer Science, vol. 10016. 2016.
A. Doroshin, Jillings, S., Jeurissen, B., Tomilovskaya, E., Pechenkova, E. V., Nosikova, I., Rumshiskaya, A., Litvinova, L., Rukavishnikov, I., De Laet, C., Schoenmaekers, C., Sijbers, J., Laureys, S. S. L., Petrovichev, V., Van Ombergen, A., Annen, J., Sunaert, S., Parizel, P. M., Sinitsyn, V., Eulenburg, Pzu, Osipowicz, K., and Wuyts, F. L., “Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight”, Front. Neural Circuits, vol. 16, 2022.
T. Dox, Heylen, R., and Scheunders, P., “Spectral variability in a multilinear mixing model”, in IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27, 2018.
J. Driesen and Scheunders, P., “A Multicomponent Image Segmentation Framework”, Lecture Notes in Computer Science, vol. 5259, pp. 589-600, 2008.
J. Driesen and Scheunders, P., “Wavelet based Filter Array Demosaicking”, in Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore, 2004, pp. 3311-3314.
J. Driesen, Thoonen, G., and Scheunders, P., “Spatial hyperspectral image classification by prior segmentation”, in Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009, Cape Town, South Africa, 2009, vol. 3, p. III-709 - III-712.
J. Driesen and Scheunders, P., “Wavelet based segmentation of multivalued images”, in SPIE Optics East, 23-26 October, Boston, Massachusetts USA, 2005, vol. 6001, pp. 13-22.
J. Driesen and Scheunders, P., “Wavelet based segmentation of multi-component images”, in IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS) March 28-29, Antwerp, Belgium, 2006, pp. 151-154.
A. Duijster, Scheunders, P., and De Backer, S., “Wavelet-Based EM Algorithm for Multispectral-Image Restoration”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3892-3898, 2009.
A. Duijster, De Backer, S., and Scheunders, P., “Wavelet-based Multispectral Image Restoration”, in IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008, vol. 3, pp. 79-82.
A. Duijster, De Backer, S., and Scheunders, P., “Multicomponent image restoration, an experimental study”, Lecture Notes in Computer Science, vol. 4633, pp. 58-68, 2007.
A. Duijster, De Backer, S., and Scheunders, P., “Wavelet-based Multicomponent Image Restoration”, in Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, United States, September 9-12, 2007, vol. 6763.
E
T. Elberfeld, De Beenhouwer, J., and Sijbers, J., “Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction”, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2019, vol. 11072.PDF icon Download paper (5.15 MB)
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., “Parametric Reconstruction of Advanced Glass Fiber-reinforced Polymer Composites from X-ray Images”, 8th Conference on Industrial Computed Tomography. Wels, Austria, 2018.PDF icon Download paper (636.09 KB)
T. Elberfeld, De Beenhouwer, J., den Dekker, A. J., Heinzl, C., and Sijbers, J., “Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data - A Simulation Study”, Journal of Nondestructive Evaluation, vol. 37, no. 62, pp. 1573-4862, 2018.
T. Elberfeld, Bazrafkan, S., De Beenhouwer, J., and Sijbers, J., “Mixed-Scale Dense Convolutional Neural Network based Improvement of Glass Fiber-reinforced Composite CT Images”, 4th International Conference on Tomography of Materials & Structures. 2019.
T. Elberfeld, Fröhler, B., Heinzl, C., Sijbers, J., and De Beenhouwer, J., “cuPARE: Parametric Reconstruction of Curved Fibres from Glass fibre-reinforced Composites”, Nondestructive Testing and Evaluation, 2022.PDF icon Download paper (9.66 MB)
T. Elberfeld, “Parametric Fiber Analysis for Glass Fiber-reinforced Composite Tomographic Images”, 2023.PDF icon Download PhD thesis (35.09 MB)

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