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U. Roine, Roine, T., Salmi, J., Nieminen-von Wendt, T., Leppämäki, S., Rintahaka, P., Tani, P., Leemans, A., and Sams, M., “Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: A diffusion tensor imaging study”, Autism Research, vol. 6, no. 6, pp. 642-650, 2013.PDF icon Download paper (1.2 MB)
T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Leemans, A., Philips, W., and Sijbers, J., “Isotropic non-white matter partial volume effects in constrained spherical deconvolution”, Information-based methods for neuroimaging: analyzing structure, function and dynamics, p. 112, 2015.
T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Leemans, A., Philips, W., and Sijbers, J., “Isotropic non-white matter partial volume effects in constrained spherical deconvolution”, Frontiers in Neuroinformatics, vol. 8, pp. 1-9, 2014.PDF icon Download paper (1.79 MB)
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.
M. Roshani, Sattari, M. Amir, Ali, P. Jammal Muh, Roshani, G. Hossein, Corniani, E., and Nazemi, E., “Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter”, Flow Measurement and Instrumentation, vol. 75, 2020.
M. Roshani, Phan, G. T. T., Roshani, G. Hossein, Hanus, R., Duong, T., Corniani, E., Nazemi, E., and Kalmouni, E. M., “Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness”, Alexandria Engineering Journal, vol. 60, 2021.
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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)
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)
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.
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)
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)
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)
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, 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, 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)
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.

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