Publications

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Journal Article
F. Calamante, Jeurissen, B., Smith, R. E., Tournier, J. - D., and Connelly, A., The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density, Magnetic Resonance in Medicine, vol. 79, no. 5, pp. 2738–2744, 2018.
F. Calamante, Jeurissen, B., Smith, R. E., Tournier, J. - D., and Connelly, A., The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density, Magnetic Resonance in Medicine, vol. 79, no. 5, pp. 2738–2744, 2018.
M. Van Dael, Lebotsa, S., Herremans, E., Verboven, P., Sijbers, J., Opara, U. L., Cronje, U. L., and Nicolai, B., A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs, Postharvest Biology and Technology, vol. 112, pp. 205-214, 2016.
N. Chabi, Iuso, D., Beuing, O., Preim, B., and Saalfeld, S., Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography., Int J Comput Assist Radiol Surg, 2022.
G. Zhang, Scheunders, P., Cerra, D., and Muller, R., Shadow-aware nonlinear spectral unmixing for hyperspectral imagery, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5514-5533, 2022.PDF icon shadow-aware_nonlinear_spectral_unmixing_for_hyperspectral_imagery.pdf (9.51 MB)
G. Liu, Nath, T., Guo, Z., Linneweber, G., Claeys, A., Li, J., Bengochea, M., De Backer, S., Weyn, B., Sneyders, M., Nicasy, H., Yu, P., Scheunders, P., and Hassan, B., A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila, Plos Computational Biology, vol. 14, no. 8, p. e1006410, 2018.
Y. Huybrechts, De Ridder, R., De Samber, B., Boudin, E., Tonelli, F., Knapen, D., Schepers, D., De Beenhouwer, J., Sijbers, J., Forlino, A., Coucke, P., P. Witten, E., Kwon, R., Willaert, A., Hendrickx, G., and Van Hul, W., The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget’s disease of bone?, Bone Reports, vol. 16, no. 101483, pp. 75-76, 2022.
N. J. Forde, O'Donoghue, S., Scanlon, C., Emsell, L., Chaddock, C., Leemans, A., Jeurissen, B., Barker, G. J., Cannon, D. M., Murray, R. M., and others, Structural brain network analysis in families multiply affected with bipolar I disorder, Psychiatry Research: Neuroimaging, vol. 234, pp. 44–51, 2015.
N. J. Forde, O'Donoghue, S., Scanlon, C., Emsell, L., Chaddock, C., Leemans, A., Jeurissen, B., Barker, G. J., Cannon, D. M., Murray, R. M., and others, Structural brain network analysis in families multiply affected with bipolar I disorder, Psychiatry Research: Neuroimaging, vol. 234, pp. 44–51, 2015.
N. J. Forde, Ronan, L., Suckling, J., Scanlon, C., Neary, S., Holleran, L., Leemans, A., Tait, R., Rua, C., Fletcher, P. C., Jeurissen, B., Dodds, C. M., Miller, S. R., Bullmore, E. T., McDonald, C., Nathan, P. J., and Cannon, D. M., Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism., NeuroImage, vol. 90, pp. 280-9, 2014.
S. Sekar, Jonckers, E., Verhoye, M., Willems, R., Veraart, J., Van Audekerke, J., Couto, J., Giugliano, M., Wuyts, K., Dedeurwaerdere, S., Sijbers, J., Mackie, C., Ver Donck, L., Steckler, T., and Van Der Linden, A., Subchronic memantine induced concurrent functional disconnectivity and altered ultra-structural tissue integrity in the rodent brain: revealed by multimodal MRI., Psychopharmacology, vol. 227, no. 3, pp. 479-91, 2013.
L. Serbruyns, Leunissen, I., Huysmans, T., Cuypers, K., Meesen, R. L., van Ruitenbeek, P., Sijbers, J., and Swinnen, S. P., Subcortical volumetric changes across the adult lifespan: subregional thalamic atrophy accounts for age-related sensorimotor performance declines, Cortex, vol. 65, pp. 128-138, 2015.PDF icon paper (1.35 MB)
B. Koirala, Khodadadzadeh, M., Contreras, C., Zahiri, Z., Gloaguen, R., and Scheunders, P., A supervised method for nonlinear hyperspectral unmixing, Remote Sensing, vol. 11, no. 20 , 2019.PDF icon remotesensing-11-02458-v3.pdf (3.23 MB)
P. Bladt, van Osch, M. J. P., Clement, P., Achten, E., Sijbers, J., and den Dekker, A. J., Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling, Magnetic Resonance in Medicine, vol. 84, no. 5, pp. 2523-2536, 2020.PDF icon Download paper (1.3 MB)
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)
W. S. Oliveira, Teixeira, J. V., Tsang, I. R., Cavalcanti, G. D. C., and Sijbers, J., Unsupervised Retinal Vessel Segmentation Using Combined Filters, Plos One, vol. 11, pp. 1-21, 2016.
M. Siqueira Pinto, Winzeck, S., Kornaropoulos, E. N., Richter, S., Paolella, R., Correia, M. M., Glocker, B., Williams, G., Vik, A., Posti, J., Håberg, A. Kristine, Stenberg, J., Guns, P. - J., den Dekker, A. J., Menon, D. K., Sijbers, J., Van Dyck, P., and Newcombe, V. F. J., Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study, Journal of Neurotrauma, vol. 40, no. 13-14, pp. 1317-1338, 2023.
C. C. Hung, Scheunders, P., Pham, M., Su, M. C., and Coleman, T., Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification, International Journal of Fuzzy Systems, vol. 6, pp. 107-117, 2004.
K. Hufkens, Scheunders, P., and Ceulemans, R., Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada, Ecological Informatics, vol. 4, pp. 1-7, 2009.
D. Iuso, Chatterjee, S., Cornelissen, S., Verhees, D., De Beenhouwer, J., and Sijbers, J., Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models, Applied Intelligence, vol. 54, pp. 13160–13177, 2024.PDF icon Download paper (2.48 MB)
D. Iuso, Chatterjee, S., Cornelissen, S., Verhees, D., De Beenhouwer, J., and Sijbers, J., Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models, Applied Intelligence, vol. 54, pp. 13160–13177, 2024.PDF icon Download paper (2.48 MB)
H. Zivari Adab, Chalavi, S., Beets, I., Gooijers, J., Leunissen, I., Cheval, B., Collier, Q., Sijbers, J., Jeurissen, B., Swinnen, S. P., and Boisgontier, M., White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018.
H. Zivari Adab, Chalavi, S., Beets, I., Gooijers, J., Leunissen, I., Cheval, B., Collier, Q., Sijbers, J., Jeurissen, B., Swinnen, S. P., and Boisgontier, M., White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018.
H. Zivari Adab, Chalavi, S., Beets, I., Gooijers, J., Leunissen, I., Cheval, B., Collier, Q., Sijbers, J., Jeurissen, B., Swinnen, S. P., and Boisgontier, M., White matter microstructural organization of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults, European Journal of Neuroscience , vol. 47, no. 5, pp. 446–459, 2018.

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