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P. Scheunders, “A genetic Lloyd-Max image quantization algorithm”, Pattern Recognition Letters, vol. 17, pp. 547-556, 1996.
P. Scheunders and De Backer, S., “Wavelet denoising of multicomponent images, using Gaussian Scale Mixture models and a noise-free image as priors”, IEEE Transactions on Image Processing, vol. 16, pp. 1865-1872, 2007.
P. Scheunders, “A genetic c-means clustering algorithm applied to color image quantization”, Pattern Recognition, vol. 30, pp. 859-866, 1997.
P. Scheunders, “Comparison of clustering algorithms applied to colour image quantization”, Pattern Recognition Letters, vol. 18, pp. 1379-1384, 1997.
P. Scheunders, Livens, S., Van de Wouwer, G., Vautrot, P., and Van Dyck, D., “Wavelet-based texture analysis”, Intern. Journal on Computer Science and Information Management, vol. 1, pp. 22-34, 1998.
P. Scheunders, “Joint quantization and error-diffusion of color images using competitive learning”, Journal of the IEE Proceedings, Vision, Image and Signal Processing, vol. 14, pp. 137-140, 1998.
P. Scheunders, De Backer, S., and Naud, A., “Non-linear mapping for feature extraction”, Lecture Notes in Computer Science, vol. 1451, pp. 823-830, 1998.
P. Scheunders and De Backer, S., “Fusion and merging of multispectral images using multiscale fundamental forms”, Journal of the Optical Society of America A, vol. 18, pp. 2468-2477, 2001.
P. Scheunders, “Local mapping for multispectral image visualisation”, Image and Vision Computing, vol. 19, pp. 971-978, 2001.
P. Scheunders and De Backer, S., “High-dimensional clustering using frequency sensitive competitive learning”, Pattern Recognition, vol. 32, pp. 193-202, 1999.
P. Scheunders, “A multivalued image wavelet representation based on multiscale fundamental forms”, IEEE Transactions on Image Processing, vol. 11, pp. 568-575, 2002.
P. Scheunders, “An orthogonal wavelet representation of multivalued images”, IEEE Transactions on Image Processing, vol. 12, pp. 718-725, 2003.
J. Sanctorum, Adriaens, D., Dirckx, J. J. J., Sijbers, J., Van Ginneken, C., Aerts, P., and Van Wassenbergh, S., “Methods for characterization and optimisation of measuring performance of stereoscopic x-ray systems with image intensifiers”, Measurement Science and Technology, vol. 30, no. 10, 2019.
J. Sanctorum, Van Wassenbergh, S., Nguyen, V., De Beenhouwer, J., Sijbers, J., and Dirckx, J. J. J., “Projection-angle-dependent distortion correction in high-speed image-intensifier-based x-ray computed tomography”, Measurement Science and Technology, vol. 32, no. 035404, pp. 1-11, 2021.
J. Sanctorum, De Beenhouwer, J., and Sijbers, J., “X-ray phase contrast simulation for grating-based interferometry using GATE”, Optics Express, vol. 28, no. 22, pp. 33390-33412, 2020.PDF icon Download paper (2.5 MB)
J. Sanctorum, Sijbers, J., and De Beenhouwer, J., “Virtual grating approach for Monte Carlo simulations of edge illumination-based x-ray phase contrast imaging”, Optics Express, vol. 31, no. 21, pp. 38695-38708, 2022.PDF icon Download paper (2.95 MB)
J. Sanctorum, Van Wassenbergh, S., Nguyen, V., De Beenhouwer, J., Sijbers, J., and Dirckx, J. J. J., “Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique”, Physics in Medicine and Biology, vol. 66, no. 16, 2021.PDF icon Download paper (4.28 MB)
K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Scheunders, P., and Gloaguen, R., “Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 284-296, 2022.PDF icon mdc_jstars-final_version.pdf (6.53 MB)
K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Jackisch, R., Scheunders, P., and Gloaguen, R., “Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm”, Remote Sensing, vol. 12 (23), no. 4007, 2020.PDF icon remotesensing-12-04007-v2.pdf (21.88 MB)
K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Gloaguen, R., and Scheunders, P., “MS2A-Net: multi-view spectral-spatial association network for hyperspectral image clustering”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 6518-6530, 2022.PDF icon ms2a-net_multiscale_spectralspatial_association_network_for_hyperspectral_image_clustering.pdf (12.33 MB)
C. A. Sage, Van Hecke, W., Peeters, R. R., Sijbers, J., Robberecht, W., Parizel, P. M., Marchal, G., Leemans, A., and Sunaert, S., “Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited.”, Human brain mapping, vol. 30, no. 11, pp. 3657-75, 2009.
E. Ribeiro Sabidussi, Klein, S., Jeurissen, B., and Poot, D. H. J., “dtiRIM: A generalisable deep learning method for diffusion tensor imaging”, Neuroimage, vol. 269, 2023.PDF icon Download paper (5.11 MB)
E. Ribeiro Sabidussi, Klein, S., Caan, M., Bazrafkan, S., den Dekker, A. J., Sijbers, J., Niessen, W. J., and Poot, D. H. J., “Recurrent Inference Machines as inverse problem solvers for MR relaxometry”, Medical Image Analysis, vol. 74, pp. 1-11, 2021.PDF icon Download paper (2.26 MB)
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M. Roshani, Phan, G., Faraj, R. Hassan, Phan, N. - H., Roshani, G. Hossein, Corniani, E., and Nazemi, E., “Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products”, Nuclear Engineering and Technology, 2020.PDF icon 1-s2.0-s1738573320308779-main.pdf (1.38 MB)
M. Roshani, Phan, G., Roshani, G. Hossein, Hanus, R., Corniani, E., and Nazemi, E., “Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows”, Measurement, vol. 168, 2021.

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