@article {2453,
	title = {Longitudinal brain-age predictions comprising long-duration spaceflight missions},
	journal = {npj Microgravity},
	volume = {12},
	year = {2026},
	month = {Jan-12-2026},
	doi = {10.1038/s41526-026-00575-3},
	url = {https://www.nature.com/articles/s41526-026-00575-3https://www.nature.com/articles/s41526-026-00575-3.pdfhttps://www.nature.com/articles/s41526-026-00575-3https://www.nature.com/articles/s41526-026-00575-3.pdf},
	author = {Tang, Ge and Patil, Kaustubh R. and Hoffstaedter, Felix and More, Shammi and Eickhoff, Simon B. and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Gerlach, Darius and Nosikova, Inna and Riabova, Alexandra and Pechenkova, Ekaterina and Petrovichev, Viktor and Rukavishnikov, Ilya and Makovskaya, Lyudmila and Ombergen, Angelique Van and Wuyts, Floris L. and Eulenburg, Peter zu}
}
@article {2431,
	title = {Physiological cerebrospinal fluid interactions between brain and eye structures are altered after long-duration spaceflight},
	journal = {Experimental Physiology},
	year = {2026},
	doi = {http://doi.org/10.1113/EP093112},
	author = {Ge Tang and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Inna Nosikova and Alexandra Riabova and Ekaterina V. Pechenkova and Petrovichev, Victor and Ilya Rukavishnikov and Stefan Sunaert and Paul M Parizel and Lyudmila Makovskaya and Valentin Sinitsyn and Jan Sijbers and Jitka Annen and Steven S L Laureys and Angelique Van Ombergen and Seyed-Ahmad Ahmadi and Floris L Wuyts and Peter zu Eulenburg}
}
@article {2456,
	title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2{\textemdash}Ex vivo imaging: Added value and acquisition},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2535 - 2560},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30435},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30435https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30435},
	author = {Schilling, Kurt G. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Howard, Amy F. D. and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@article {2457,
	title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3{\textemdash}Ex vivo imaging: Data processing, comparisons with microscopy, and tractography},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2561 - 2582},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30424},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30424},
	author = {Schilling, Kurt G. and Howard, Amy F. D. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Jelescu, Ileana O.}
}
@article {2455,
	title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging},
	journal = {Magnetic Resonance in Medicine},
	volume = {93},
	year = {2025},
	month = {Jan-06-2025},
	pages = {2507 - 2534},
	issn = {0740-3194},
	doi = {10.1002/mrm.v93.610.1002/mrm.30429},
	url = {https://onlinelibrary.wiley.com/toc/15222594/93/6https://onlinelibrary.wiley.com/doi/10.1002/mrm.30429https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30429},
	author = {Jelescu, Ileana O. and Grussu, Francesco and Ianus, Andrada and Hansen, Brian and Barrett, Rachel L. C. and Aggarwal, Manisha and Michielse, Stijn and Nasrallah, Fatima and Syeda, Warda and Wang, Nian and Veraart, Jelle and Roebroeck, Alard and Bagdasarian, Andrew F. and Eichner, Cornelius and Sepehrband, Farshid and Zimmermann, Jan and Soustelle, Lucas and Bowman, Christien and Tendler, Benjamin C. and Hertanu, Andreea and Ben Jeurissen and Verhoye, Marleen and Frydman, Lucio and van de Looij, Yohan and Hike, David and Dunn, Jeff F. and Miller, Karla and Landman, Bennett A. and Shemesh, Noam and Anderson, Adam and McKinnon, Emilie and Farquharson, Shawna and Dell{\textquoteright}Acqua, Flavio and Pierpaoli, Carlo and Drobnjak, Ivana and Leemans, Alexander and Harkins, Kevin D. and Descoteaux, Maxime and Xu, Duan and Huang, Hao and Santin, Mathieu D. and Grant, Samuel C. and Obenaus, Andre and Kim, Gene S. and Wu, Dan and Le Bihan, Denis and Blackband, Stephen J. and Ciobanu, Luisa and Els Fieremans and Bai, Ruiliang and Leergaard, Trygve B. and Zhang, Jiangyang and Dyrby, Tim B. and Johnson, G. Allan and Cohen-Adad, Julien and Budde, Matthew D. and Schilling, Kurt G.}
}
@inproceedings {2400,
	title = {In-depth imaging of in-vitro tissue models using electrical impedance tomography},
	booktitle = {iCT 2025 - 14th Conference on Industrial Computed Tomography},
	volume = {4 - 7 February},
	year = {2025},
	doi = {doi.org/10.58286/30763},
	author = {Jian Deng and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Yoke Chin Chai and Tom Torfs and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@article {2399,
	title = {Loss of the Ubiquitin-Associated Domain of sqstm1/p62 in Zebrafish Causes a Phenotype Resembling Paget{\textquoteright}s Disease of Bone},
	journal = {Calcified Tissue International},
	volume = {116},
	year = {2025},
	pages = {1-15},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Dylan Bergen and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Lucia Vergauwen and Dorien Schepers and Evelien Van Dijck and Qiao Tong and Anja Verhulst and Jan De Beenhouwer and Jan Sijbers and Chrissy Hammond and Antonella Forlino and Geert Mortier and Paul Coucke and P Eckhard Witten and Ronald Young Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@article {2454,
	title = {MReye-Seg: development and validation of an automated MRI pipeline for standardised ocular and orbital morphometrics},
	journal = {Eye},
	volume = {39},
	year = {2025},
	month = {Jan-12-2025},
	pages = {3294 - 3305},
	issn = {0950-222X},
	doi = {10.1038/s41433-025-04044-1},
	url = {https://www.nature.com/articles/s41433-025-04044-1https://www.nature.com/articles/s41433-025-04044-1.pdfhttps://www.nature.com/articles/s41433-025-04044-1.pdfhttps://www.nature.com/articles/s41433-025-04044-1},
	author = {Tang, Ge and Ahmadi, Seyed-Ahmad and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Nosikova, Inna and Ryabova, Alexandra and Pechenkova, Ekaterina and Petrovichev, Viktor and Rukavishnikov, Ilya and Velten, Theresa and Gripshi, Marina and Van Ombergen, Angelique and Wuyts, Floris L. and zu Eulenburg, Peter}
}
@article {2421,
	title = {A Spectral-Spatial Attention Network for Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {63},
	year = {2025},
	pages = {1-15},
	abstract = {Hyperspectral unmixing, an essential and fundamental task in remote sensing, focuses on estimating endmembers (spectrally pure components) and their fractional abundances within each mixed pixel of a hyperspectral image. With the advent of deep learning (DL), the field of hyperspectral unmixing has made significant progress. Among DL approaches, autoencoder-based models have shown promising results. However, most unmixing methods estimate the endmembers by the weights of the linear layers in the decoder of their networks, making their performance highly dependent on weight initialization. Moreover, noise is not explicitly accounted for in most recent methods that use spectral angle distance (SAD) loss. To avoid the initialization problems, we developed an innovative inversion strategy to directly estimate the endmembers. Moreover, to optimally account for noise, an end-to-end network is proposed, which integrates both denoising and unmixing. Finally, for an improved feature extraction, a novel spectral-spatial attention module (SSAM) is integrated into the network. Extensive experiments on synthetic and three real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods. The full code is available at https://github.com/xuanwentao for public evaluation.},
	keywords = {Attention module, autoencoder, Autoencoders, autonomous unmixing module (AUM), Convolutional neural networks, Data mining, Decoding, deep denoising module (DDM), Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Loss measurement, Neural networks, Noise reduction},
	doi = {10.1109/TGRS.2025.3576479},
	author = {Tao, Xuanwen and Koirala, Bikram and Rasti, Behnood and Plaza, Antonio and Paul Scheunders}
}
@article {2459,
	title = {Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington{\textquoteright}s disease},
	journal = {Neurobiology of Disease},
	volume = {193},
	year = {2024},
	month = {Jan-04-2024},
	pages = {106438},
	issn = {09699961},
	doi = {10.1016/j.nbd.2024.106438},
	author = {Vidas-Guscic, Nicholas and van Rijswijk, Jo{\"e}lle and Van Audekerke, Johan and Ben Jeurissen and Nnah, Israel and Tang, Haiying and Mu{\~n}oz-Sanjuan, Ignacio and Pustina, Dorian and Cachope, Roger and Van der Linden, Annemie and Bertoglio, Daniele and Verhoye, Marleen}
}
@conference {2460,
	title = {Fixel-based analysis of multi-shell diffusion MRI reveals white matter changes after long-duration spaceflight},
	year = {2024},
	pages = {2057},
	author = {Ben Jeurissen and Steven Jillings and Giraldo, Diana L and Van Ombergen, Angelique and Tomilovskaya, Elena and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Petrovichev, Victor and Sijbers, Jan and zu Eulenburg, Peter and Wuyts, Floris L}
}
@article {2347,
	title = {Imaging Algorithm and Optimal Measurement Protocol for 3D Impedance Tomography on 2D High Density Micro Electrode Array},
	journal = {IEEE Sensors},
	volume = {24},
	year = {2024},
	pages = {24452-24465},
	doi = {10.1109/JSEN.2024.3417289},
	author = {Jian Deng and Jens Renders and Babu Linkoon P Meenaketan and Saeedeh Ebrahimi Takalloo and Dries Braeken and Jan De Beenhouwer and Jan Sijbers}
}
@article {2382,
	title = {A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2024},
	pages = {1-1},
	keywords = {Accuracy, dual-feature fusion, Estimation, Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Noise reduction, Reflectivity, Scattering, similarity, Transformers, Transforms, unmixing module},
	doi = {10.1109/TGRS.2024.3505292},
	author = {Tao, Xuanwen and Koirala, Bikram and Plaza, Antonio and Paul Scheunders}
}
@conference {2461,
	title = {Iterative model-based Rician bias correction and its application to denoising in diffusion MRI},
	year = {2023},
	pages = {3795},
	author = {Tournier, Jacques-Donald and Ben Jeurissen and Christiaens, Daan}
}
@article {2270,
	title = {Prolonged microgravity induces reversible and persistent changes on human cerebral connectivity},
	journal = {Communications Biology},
	volume = {6},
	year = {2023},
	doi = {https://doi.org/10.1038/s42003-022-04382-w},
	author = {Steven Jillings and Ekaterina V. Pechenkova and Elena Tomilovskaya and Ilya Rukavishnikov and Ben Jeurissen and Angelique Van Ombergen and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Annen, Jitka and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Petrovichev, Victor and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, P and Steven S L Laureys and Athena Demertzi and Floris L Wuyts}
}
@article {2285,
	title = {Towards material and process agnostic features for the classification of pore types in metal additive manufacturing},
	journal = {Materials \& Design},
	volume = {227},
	year = {2023},
	pages = {111757},
	doi = {10.1016/j.matdes.2023.111757},
	author = {M. Vandecasteele and R. Heylen and Domenico Iuso and A. Thanki and W. Philips and A. Witvrouw and D. Verhees and B. G. Booth}
}
@article {2204,
	title = {3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts},
	journal = {Measurement Science and Technology},
	volume = {33},
	year = {2022},
	pages = {1-12},
	doi = {10.1088/1361-6501/ac459a},
	author = {Rob Heylen and Aditi Thanki and Dries Verhees and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers and Ann Witvrouw and Han Haitjema and Abdellatif Bey-Temsamani}
}
@article {2216,
	title = {Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight},
	journal = {Front. Neural Circuits},
	volume = {16},
	year = {2022},
	doi = {https://doi.org/10.3389/fncir.2022.815838},
	author = {Andrei Doroshin and Steven Jillings and Ben Jeurissen and Elena Tomilovskaya and Ekaterina V. Pechenkova and Nosikova, Inna and Alena Rumshiskaya and Liudmila Litvinova and Ilya Rukavishnikov and Chloe De Laet and Catho Schoenmaekers and Jan Sijbers and Steven S L Laureys and Petrovichev, Victor and Angelique Van Ombergen and Jitka Annen and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and zu Eulenburg, Peter and Karol Osipowicz and Floris L Wuyts}
}
@article {2215,
	title = {The effect of prolonged Spaceflight on Cerebrospinal Fluid and Perivascular Spaces of Astronauts and Cosmonauts},
	journal = {PNAS},
	volume = {119},
	year = {2022},
	chapter = {e2120439119},
	doi = {https://doi.org/10.1073/pnas.2120439119},
	author = {Giuseppe Barisano and Farshid Sepehrband and Heather R. Collins and Steven Jillings and Ben Jeurissen and Andrew J. Taylor and Catho Schoenmaekers and Chloe De Laet and Ilya Rukavishnikov and Inna Nosikova and Alena Rumshiskaya and Jitka Annen and Jan Sijbers and Steven S L Laureys and Angelique Van Ombergen and Petrovichev, Victor and Valentin Sinitsyn and Ekaterina V. Pechenkova and Alexey Grishin and Peter zu Eulenburg and Meng Law and Stefan Sunaert and Paul M. Parizel and Elena Tomilovskaya and Donna Roberts and Floris L Wuyts}
}
@article {2210,
	title = {Feature preserving non-rigid iterative weighted closest point and semi-curvature registration},
	journal = {IEEE Transactions on Image Processing},
	year = {2022},
	abstract = {Preserving features of a surface as characteristic local shape properties captured e.g. by curvature, during non-rigid registration is always difficult where finding meaningful correspondences, assuring the robustness and the convergence of the algorithm while maintaining the quality of mesh are often challenges due to the high degrees of freedom and the sensitivity to features of the source surface. In this paper, we present a non-rigid registration method utilizing a newly defined semi-curvature, which is inspired by the definition of the Gaussian curvature. In the procedure of establishing the correspondences, for each point on the source surface, a corresponding point on the target surface is selected using a dynamic weighted criterion defined on the distance and the semi-curvature. We reformulate the cost function as a combination of the semi-curvature, the stiffness, and the distance terms, and ensure to penalize errors of both the distance and the semi-curvature terms in a guaranteed stable region. For a robust and efficient optimization process, we linearize the semi-curvature term, where the region of attraction is defined and the stability of the approach is proven. Experimental results show that features of the local areas on the original surface with higher curvature values are better preserved in comparison with the conventional methods. In comparison with the other methods, this leads to, on average, 75\%, 8\% and 82\% improvement in terms of quality of correspondences selection, quality of surface after registration, and time spent of the convergence process respectively, mainly due to that the semi-curvature term logically increases the constraints and dependency of each point on the neighboring vertices based on the point{\textquoteright}s degree of curvature.},
	keywords = {curvature, non-linearity, Non-rigid registration, region of attraction},
	issn = {1057-7149},
	doi = {10.1109/TIP.2022.3148822},
	author = {Farzam Tajdari and Toon Huysmans and Yusheng Yang and Yu Song}
}
@article {2234,
	title = {Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images},
	journal = {Computers and Electronics in Agriculture},
	volume = {197},
	year = {2022},
	pages = {1-14},
	doi = {https://doi.org/10.1016/j.compag.2022.106962},
	author = {Tim Van De Looverbosch and Jiaqi, He and Astrid Tempelaere and Klaas Kelchtermans and Pieter Verboven and Tinne Tuytelaars and Jan Sijbers and Bart Nicolai}
}
@article {2249,
	title = {The sqstm1tmΔUBA zebrafish model, a proof-of-concept in vivo model for Paget{\textquoteright}s disease of bone?},
	journal = {Bone Reports},
	volume = {16},
	year = {2022},
	pages = {75-76},
	doi = {10.1016/j.bonr.2022.101483},
	author = {Yentl Huybrechts and Rapha{\"e}l De Ridder and Bjorn De Samber and Eveline Boudin and Francesca Tonelli and Dries Knapen and Dorien Schepers and Jan De Beenhouwer and Jan Sijbers and Antonella Forlino and Paul Coucke and P. Eckhard Witten and Ronald Kwon and Andy Willaert and Gretl Hendrickx and Wim Van Hul}
}
@inproceedings {2223,
	title = {Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2021},
	doi = {10.1109/WHISPERS52202.2021.9484033},
	author = {Ting Hu and N. Liu and W. Li and R. Tao and F. Zhang and Paul Scheunders}
}
@conference {2464,
	title = {On false positive control in Fixel-Based Analysis},
	volume = {29},
	year = {2021},
	author = {Smith, R E and Christiaens, D and Ben Jeurissen and Pietsch, M and Vaughan, D N and Jackson, G and Tournier, J D}
}
@article {2066,
	title = {Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {59},
	year = {2021},
	pages = {1516-1533},
	author = {T. Hu and W. Li and N. Liu and R. Tao and F. Zhang and Paul Scheunders}
}
@article {2211,
	title = {Brain Computer Interface},
	year = {2020},
	month = {01/2020},
	publisher = {Universiteit Antwerpen},
	chapter = {EP 3 597 102 A1; A61B 5/04 (2006.01),A61B 5/0476 (2006.01),A61B 5/00 (2006.01)"},
	address = {Belgium},
	abstract = {The present invention relates to an interfacingsystem for interfacing a plurality of elements to a surfaceof interest. The elements thereby are suitable for locallysensing a signal of the surface of interest or for locallyactuating the surface of interest. The interfacing systemcomprises a plurality of element holders, each elementholder being configured for holding an element for interfacingwith the surface of interest and each element holderbeing configured for providing a pressure exerted onthe element for interfacing the element with the surfaceof interest, the pressure being based on a fluidic actionin the element holder. The interfacing system also comprisesan outer shell interconnecting the plurality of elementholders. At least two of the plurality of element holdersfurther are fluidically interconnected so as to link thefluidic action in the at least two element holders therebylinking the pressure exerted by the element holders onthe elements.},
	author = {Stijn Verwulgen and Daniel Lacko and Jochen Vleugels and Toon Huysmans and Steven Truijen}
}
@conference {Jillings2020-nb,
	title = {Diffusion MRI reveals macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	year = {2020},
	pages = {4531},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Rumshiskaya, A and Litvinova, L and Nosikova, I and Pechenkova, E and Rukavishnikov, I and Kozlovskaya, I and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, V and Laureys, S and zu Eulenburg, P and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@article {2074,
	title = {Macro- and microstructural changes in cosmonauts{\textquoteright} brains after long-duration spaceflight},
	journal = {Science Advances},
	volume = {6},
	year = {2020},
	month = {Apr-09-2020},
	pages = {eaaz9488},
	abstract = {Long-duration spaceflight causes widespread physiological changes, although its effect on brain structure remains poorly understood. In this work, we acquired diffusion magnetic resonance imaging to investigate alterations of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compositions in each voxel, before, shortly after, and 7 months after long-duration spaceflight. We found increased WM in the cerebellum after spaceflight, providing the first clear evidence of sensorimotor neuroplasticity. At the region of interest level, this increase persisted 7 months after return to Earth. We also observe a widespread redistribution of CSF, with concomitant changes in the voxel fractions of adjacent GM. We show that these GM changes are the result of morphological changes rather than net tissue loss, which remained unclear from previous studies. Our study provides evidence of spaceflight-induced neuroplasticity to adapt motor strategies in space and evidence of fluid shift{\textendash}induced mechanical changes in the brain.},
	doi = {10.1126/sciadv.aaz9488},
	url = {https://advances.sciencemag.org/content/6/36/eaaz9488},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and Kozlovskaya, Inessa B. and Manko, Olga and Danilichev, Sergey and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Petrovichev, Victor and Laureys, Steven and zu Eulenburg, Peter and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@conference {2139,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Piet Bladt and Quinten Beirinckx and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@conference {Bladt2020-mp,
	title = {Super-resolution reconstruction of single-PLD pseudo-continuous ASL images},
	year = {2020},
	pages = {3293},
	author = {Bladt, P and Beirinckx, Q and Van der Plas, M and Schmid, S and Teeuwisse, W and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and van Osch, M}
}
@conference {2015,
	title = {Super-resolution strategies for single-PLD pseudo-continuous ASL},
	year = {2020},
	address = {Arnhem, The Netherlands},
	abstract = {Super-resolution reconstruction (SRR) allows for 3D high-resolution image reconstruction from a set of low-resolution multi-slice images with different orientations. Arterial spin labeling (ASL) is an interesting albeit complicated candidate for SRR, as it relies on subtraction. SRR-ASL can be performed on low-SNR subtracted or on low-contrast unsubtracted ASL data. Different ASL-SRR implementations were applied to single-PLD PCASL data and validated against traditional ASL-scans. Combining motion correction, super-resolution post-processing and pairwise subtraction of label-control pairs in a single framework yielded comparable CBF maps as with traditional HR-ASL. Furthermore, in certain slices, SRR-ASL appears to reconstruct the anatomical structure with higher fidelity.},
	author = {Quinten Beirinckx and Piet Bladt and Merlijn C E van der Plas and Sophie Schmid and Wouter M Teeuwisse and Ben Jeurissen and Arnold Jan den Dekker and Jan Sijbers and M.J.P van Osch}
}
@article {1964,
	title = {Alterations of Functional Brain Connectivity After Long-Duration Spaceflight as Revealed by fMRI.},
	journal = {Front Physiol},
	volume = {10},
	year = {2019},
	pages = {1-23},
	abstract = {<p>The present study reports alterations of task-based functional brain connectivity in a group of 11 cosmonauts after a long-duration spaceflight, compared to a healthy control group not involved in the space program. To elicit the postural and locomotor sensorimotor mechanisms that are usually most significantly impaired when space travelers return to Earth, a plantar stimulation paradigm was used in a block design fMRI study. The motor control system activated by the plantar stimulation involved the pre-central and post-central gyri, SMA, SII/operculum, and, to a lesser degree, the insular cortex and cerebellum. While no post-flight alterations were observed in terms of activation, the network-based statistics approach revealed task-specific functional connectivity modifications within a broader set of regions involving the activation sites along with other parts of the sensorimotor neural network and the visual, proprioceptive, and vestibular systems. The most notable findings included a post-flight increase in the stimulation-specific connectivity of the right posterior supramarginal gyrus with the rest of the brain; a strengthening of connections between the left and right insulae; decreased connectivity of the vestibular nuclei, right inferior parietal cortex (BA40) and cerebellum with areas associated with motor, visual, vestibular, and proprioception functions; and decreased coupling of the cerebellum with the visual cortex and the right inferior parietal cortex. The severity of space motion sickness symptoms was found to correlate with a post- to pre-flight difference in connectivity between the right supramarginal gyrus and the left anterior insula. Due to the complex nature and rapid dynamics of adaptation to gravity alterations, the post-flight findings might be attributed to both the long-term microgravity exposure and to the readaptation to Earth{\textquoteright}s gravity that took place between the landing and post-flight MRI session. Nevertheless, the results have implications for the multisensory reweighting and gravitational motor system theories, generating hypotheses to be tested in future research.</p>},
	issn = {1664-042X},
	doi = {10.3389/fphys.2019.00761},
	author = {Pechenkova, Ekaterina and Nosikova, Inna and Rumshiskaya, Alena and Litvinova, Liudmila and Rukavishnikov, Ilya and Mershina, Elena and Valentin Sinitsyn and Angelique Van Ombergen and Ben Jeurissen and Steven Jillings and Laureys, Steven and Jan Sijbers and Grishin, Alexey and Chernikova, Ludmila and Naumov, Ivan and Kornilova, Ludmila and Floris L Wuyts and Tomilovskaya, Elena and Kozlovskaya, Inessa}
}
@article {1941,
	title = {Brain ventricular volume changes induced by long-duration spaceflight},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	pages = {10531-10536},
	abstract = {Long-duration spaceflight induces detrimental changes in human physiology due to microgravity. One example is a cephalic fluid shift. Here, we prospectively investigated the quantitative changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest, observer-independent analysis on structural brain MRI scans. MRI scans were collected before the mission, shortly after and 7 mo after return to Earth. We found a significant increase in lateral and third ventricles at postflight and a trend to normalization at follow-up, but still significantly increased ventricular volumes. The observed spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause.Long-duration spaceflight induces detrimental changes in human physiology. Its residual effects and mechanisms remain unclear. We prospectively investigated the changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest analysis on structural brain scans. Cosmonaut MRI data were investigated preflight (n = 11), postflight (n = 11), and at long-term follow-up 7 mo after landing (n = 7). Post hoc analyses revealed a significant difference between preflight and postflight values for all supratentorial ventricular structures, i.e., lateral ventricle (mean \% change {\textpm} SE = 13.3 {\textpm} 1.9), third ventricle (mean \% change {\textpm} SE = 10.4 {\textpm} 1.1), and the total ventricular volume (mean \% change {\textpm} SE = 11.6 {\textpm} 1.5) (all P \&lt; 0.0001), with higher volumes at postflight. At follow-up, these structures did not quite reach baseline levels, with still residual increases in volume for the lateral ventricle (mean \% change {\textpm} SE = 7.7 {\textpm} 1.6; P = 0.0009), the third ventricle (mean \% change {\textpm} SE = 4.7 {\textpm} 1.3; P = 0.0063), and the total ventricular volume (mean \% change {\textpm} SE = 6.4 {\textpm} 1.3; P = 0.0008). This spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause. Our results warrant more detailed and longer longitudinal follow-up. The clinical impact of our findings on the long-term cosmonauts{\textquoteright} health and their relation to ocular changes reported in space travelers requires further prospective studies.},
	issn = {0027-8424},
	doi = {10.1073/pnas.1820354116},
	url = {https://www.pnas.org/content/early/2019/05/01/1820354116},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and Alena Rumshiskaya and Liudmila Litvinova and Nosikova, Inna and Ekaterina V. Pechenkova and Ilya Rukavishnikov and Manko, Olga and Danylichev, Sergey and R{\"u}hl, R. Maxine and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@inproceedings {1850,
	title = {Determining Comfortable Pressure Ranges for Wearable EEG Headsets},
	booktitle = {Advances in Human Factors in Wearable Technologies and Game Design},
	year = {2019},
	pages = {11{\textendash}19},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Measuring and interpretation of brain wave signals through electroencephalography (EEG) is an emerging technology. The technique is traditionally applied in a clinical setting with EEG caps and conductive gels to ensure proper contact through a subject{\textquoteright}s hair, and anticipate inter-subject anthropometric variations. Development of dry electrodes offers the potential to develop wearable EEG headsets. Such devices could induce medical and commercial applications. In this paper, we evaluate a prototype EEG headset that actively places electrodes at standardized positions on the subject{\textquoteright}s head, where each electrode is applied with equal pressure. The system is designed for use with dry electrodes. Our research delivers a better understanding on the link between general level of comfort and possible useful clear data signals, that can be used in brain computer interfaces (BCI). The present study is confined to the impact of adjustable electrodes pressure on level of user comfort only. Levels of discomfort are assessed in twelve participants, wearing an EEG headset with controllable electrode pressure exerted at 14 locations. Of-the-shelf dry electrodes are used. In a first session, evenly distributed pressure is increased and afterwards decreased in fixed time intervals, going from 10~kPa to 30~kPa and vice versa with steps of 2~kPa. In a second session, a subject specific acceptable pressure level is retrieved from the data of the first session and constantly applied for 30~min. During this intervention, level of discomfort is assessed in a VAS-scale. Additional observation and surveys yields insights on user experience in wearing a pressure exerting EEG headset.},
	isbn = {978-3-319-94619-1},
	author = {Stijn Verwulgen and Daniel Lacko and Justine, Hoppenbrouwers and Kustermans, Siemon and Moons, Stine and Thys, Falk and Zelck, Sander and Vaes, Kristof and Toon Huysmans and Jochen Vleugels and Steven Truijen},
	editor = {Ahram, Tareq Z.}
}
@conference {Jillings2019-al,
	title = {Diffusion-weighted imaging reveals structural brain changes in cosmonauts after long-duration spaceflight},
	volume = {32 (Suppl. 1)},
	year = {2019},
	pages = {Magn Reson Mater Phy. 2019; 32(Suppl. 1):S101},
	author = {Steven Jillings and Angelique Van Ombergen and Tomilovskaya, E and Laureys, S and zu Eulenburg, P and Stefan Sunaert and Jan Sijbers and Floris L Wuyts and Ben Jeurissen}
}
@inproceedings {1873,
	title = {Moving Statistical Body Shape Models Using Blender},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)},
	year = {2019},
	pages = {28{\textendash}38},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper, we present a new framework to integrate movement acquired by a motion capture system to a statistical body shape model using Blender. This provides a visualization of a digital human model based upon anthropometry and biomechanics of the subject. A moving statistical body shape model helps to visualize physical tasks with inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modeling approach is useful for reliable prediction and simulation of the body shape movement of a specific population with a few given predictors such as stature, body mass index and age.},
	isbn = {978-3-319-96077-7},
	doi = {10.1007/978-3-319-96077-7_4},
	author = {Scataglini, Sofia and Femke Danckaers and Haelterman, Robby and Toon Huysmans and Jan Sijbers},
	editor = {Bagnara, Sebastiano and Tartaglia, Riccardo and Albolino, Sara and Alexander, Thomas and Fujita, Yushi}
}
@article {1963,
	title = {MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.},
	journal = {Neuroimage},
	year = {2019},
	month = {2019 Aug 29},
	pages = {116137},
	abstract = {<p>MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.</p>},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2019.116137},
	author = {Tournier, J-Donald and Smith, Robert and Raffelt, David and Tabbara, Rami and Dhollander, Thijs and Pietsch, Maximilian and Christiaens, Daan and Ben Jeurissen and Yeh, Chun-Hung and Connelly, Alan}
}
@conference {Wuyts2019-xr,
	title = {Novel insight on effect and recovery of long-duration spaceflight on the ventricles of the space traveller{\textquoteright}s brain},
	volume = {2019},
	year = {2019},
	pages = {IAC{\textendash}19_A1_2_4_x51230},
	publisher = {International Astronautical Federation},
	author = {Floris L Wuyts and Steven Jillings and Angelique Van Ombergen and Ben Jeurissen and Tomilovskaya, Elena and Rumshiskaya, Alena and Litvinova, Liudmila and Nosikova, Inna and Pechenkova, Ekaterina and Rukavishnikov, Ilya and others}
}
@article {1893,
	title = {Posture normalization of 3D body scans},
	journal = {Ergonomics},
	volume = {62},
	year = {2019},
	pages = {834-848},
	doi = {10.1080/00140139.2019.1581262},
	author = {Femke Danckaers and Toon Huysmans and Ann Hallemans and Guido De Bruyne and Steven Truijen and Jan Sijbers}
}
@inproceedings {1874,
	title = {Using 3D Statistical Shape Models for Designing Smart Clothing},
	booktitle = {Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)},
	year = {2019},
	pages = {18{\textendash}27},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper we present an innovative approach to design smart clothing using statistical body shape modeling (SBSM) from the CAESAR{\texttrademark} dataset. A combination of different digital technologies and applications are used to create a common co-design workflow for garment design. User and apparel product design and developers can get personalized prediction of cloth sizing, fitting and aesthetics.},
	isbn = {978-3-319-96077-7},
	doi = {10.1007/978-3-319-96077-7_3},
	author = {Scataglini, Sofia and Femke Danckaers and Haelterman, Robby and Toon Huysmans and Jan Sijbers and Andreoni, Giuseppe},
	editor = {Bagnara, Sebastiano and Tartaglia, Riccardo and Albolino, Sara and Alexander, Thomas and Fujita, Yushi}
}
@article {1908,
	title = {Brain Tissue{\textendash}Volume Changes in Cosmonauts},
	journal = {New England Journal of Medicine},
	volume = {379},
	year = {2018},
	pages = {1678 - 1680},
	issn = {0028-4793},
	doi = {10.1056/NEJMc1809011},
	url = {http://www.nejm.org/doi/10.1056/NEJMc1809011},
	author = {Angelique Van Ombergen and Steven Jillings and Ben Jeurissen and Tomilovskaya, Elena and R{\"u}hl, Maxine and Alena Rumshiskaya and Nosikova, Inna and Liudmila Litvinova and Annen, Jitka and Ekaterina V. Pechenkova and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Valentin Sinitsyn and Steven S L Laureys and Jan Sijbers and zu Eulenburg, Peter and Floris L Wuyts}
}
@inproceedings {1933,
	title = {Capturing Joint Angles of the Off-Site Human Body},
	booktitle = {IEEE Sensors 2018},
	year = {2018},
	pages = {1{\textendash}4},
	publisher = {IEEE},
	organization = {IEEE},
	address = {United States},
	abstract = {Motion capture (mocap) is traditionally conducted by optical systems. These are expensive and usually limited to controlled environments. We investigated the accuracy of portable and inexpensive mocap sensor systems compared to benchmark optical systems with respect to tracking joint angles. This review summarizes the findings of 21 studies. In these studies, 228 subjects were employed, and 16 joints were tracked, spanning a range of activities. We did not find a system that is consistent and equally accurate across all joint angles for all activities (root mean square error up to 12.1 degrees). However, under some ideal conditions, the results are on par with optical mocap. Our recommendations for future research and development are to focus on tracking faster activities, activities in off-site conditions, and following standardized biomechanical models of joint angles.},
	keywords = {IMU, joint kinematics, motion capture, off-site, wearable},
	isbn = {978-1-5386-4708-0},
	doi = {10.1109/ICSENS.2018.8589711},
	author = {Raman Garimella and Thomas Peeters and Koen Beyers and Steven Truijen and Toon Huysmans and Stijn Verwulgen}
}
@inbook {1900,
	title = {Contributions of Machine Learning to Remote Sensing Data Analysis},
	booktitle = {Comprehensive Remote Sensing},
	volume = {2},
	year = {2018},
	pages = {199-243},
	author = {Paul Scheunders and Devis Tuia and G Moser}
}
@article {1817,
	title = {Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid β-induced pathology.},
	journal = {Alzheimer{\textquoteright}s Research \& Therapy },
	volume = {10},
	year = {2018},
	pages = {1-16},
	doi = {10.1186/s13195-017-0329-8},
	author = {Jelle Praet and Nikolay Manyakov and Leacky Muchene and Zhenhua Mai and Vasilis Terzopoulos and Steve De Backer and An Torremans and Pieter-Jan Guns and Tom Van De Casteele and Astrid Bottelbergs and Bianca Van Broeck and Jan Sijbers and Dirk Smeets and Ziv Shkedy and Luc Bijnens and Darrel Pemberton and Mark Schmidt and Annemie Van Der Linden and Marleen Verhoye}
}
@inproceedings {1769,
	title = {Full Body Statistical Shape Modeling with Posture Normalization},
	booktitle = {The 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017)},
	volume = {591},
	year = {2018},
	pages = {437-448},
	publisher = {Springer},
	organization = {Springer},
	address = {Los Angeles, California, USA},
	doi = {10.1007/978-3-319-60591-3_39},
	author = {Femke Danckaers and Toon Huysmans and Ann Hallemans and Guido De Bruyne and Steven Truijen and Jan Sijbers}
}
@article {1791,
	title = {IntensityPatches and RegionPatches for Image Recognition},
	journal = {Applied Soft Computing},
	volume = {62},
	year = {2018},
	pages = {176-186},
	doi = {https://doi.org/10.1016/j.asoc.2017.09.046},
	url = {http://www.sciencedirect.com/science/article/pii/S1568494617305859},
	author = {Tiago B. A. de Carvalho and Maria A. Sibaldo and Ing Jyh Tsang and George D C Cavalcanti and Jan Sijbers and Ing Ren Tsang}
}
@article {1786,
	title = {The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density},
	journal = {Magnetic Resonance in Medicine},
	volume = {79},
	year = {2018},
	pages = {2738{\textendash}2744},
	keywords = {Brain, cortex, Diffusion MRI, fiber orientation distribution, myeloarchitecture, parcellation},
	issn = {1522-2594},
	doi = {10.1002/mrm.26917},
	url = {http://dx.doi.org/10.1002/mrm.26917},
	author = {Fernando Calamante and Ben Jeurissen and Robert Elton Smith and Tournier, Jacques-Donald and Connelly, Alan}
}
@conference {Van_Dyck2017-jo,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	year = {2017},
	pages = {377},
	author = {Pieter Van Dyck and De Smet, Eline and Froeling, Martijn and Peter Verdonk and Torfs, Micha{\"e}l and Pim Pullens and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@article {1744,
	title = {Diffusion Tensor Imaging of the Anterior Cruciate Ligament Graft},
	journal = {Journal of Magnetic Resonance Imaging},
	volume = {46},
	year = {2017},
	pages = {1423{\textendash}1432},
	doi = {10.1002/jmri.25666},
	author = {Pieter Van Dyck and Martijn Froeling and Eline De Smet and Pim Pullens and Micha{\"e}l Torfs and Peter Verdonk and Jan Sijbers and Paul M Parizel and Ben Jeurissen}
}
@article {1756,
	title = {The effect of spaceflight and microgravity on the human brain},
	journal = {Journal of Neurology},
	volume = {246},
	year = {2017},
	pages = {18-22},
	doi = {10.1007/s00415-017-8427-x},
	author = {Angelique Van Ombergen and Athena Demertzi and Elena Tomilovskaya and Ben Jeurissen and Jan Sijbers and Inessa B. Kozlovskaya and Paul M Parizel and Paul H Van de Heyning and Stefan Sunaert and Steven S L Laureys and Floris L Wuyts}
}
@article {1802,
	title = {Habitat mapping and quality assessment of NATURA 2000 Heatland using airborne imaging spectroscopy},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {266},
	author = {Birgen Haest and Jeroen Vanden Borre and Toon Spanhove and Guy Thoonen and Stephanie Delalieux and Kooistra, L. and C A M{\"u}cher and D Paelinckx and Paul Scheunders and Pieter Kempeneers}
}
@article {1718,
	title = {High angular resolution diffusion-weighted imaging in mild traumatic brain injury},
	journal = {NeuroImage: Clinical},
	volume = {13},
	year = {2017},
	month = {11/2016},
	pages = {174 - 180},
	abstract = {We sought to investigate white matter abnormalities in mild traumatic brain injury (mTBI) using diffusion-weighted magnetic resonance imaging (DW-MRI). We applied a global approach based on tract-based spatial statistics skeleton as well as constrained spherical deconvolution tractography. DW-MRI was performed on 102 patients with mTBI within two months post-injury and 30 control subjects. A robust global approach considering only the voxels with a single-fiber configuration was used in addition to global analysis of the tract skeleton and probabilistic whole-brain tractography. In addition, we assessed whether the microstructural parameters correlated with age, time from injury, patient{\textquoteright}s outcome and white matter \{MRI\} hyperintensities. We found that whole-brain global approach restricted to single-fiber voxels showed significantly decreased fractional anisotropy (FA) (p = 0.002) and increased radial diffusivity (p = 0.011) in patients with mTBI compared with controls. The results restricted to single-fiber voxels were more significant and reproducible than those with the complete tract skeleton or the whole-brain tractography. \{FA\} correlated with patient outcomes, white matter hyperintensities and age. No correlation was observed between \{FA\} and time of scan post-injury. In conclusion, the global approach could be a promising imaging biomarker to detect white matter abnormalities following traumatic brain injury.},
	keywords = {Diffusion-weighted magnetic resonance imaging, Global approach, Magnetic Resonance Imaging, probabilistic tractography, Traumatic brain injury},
	issn = {2213-1582},
	doi = {http://dx.doi.org/10.1016/j.nicl.2016.11.016},
	url = {http://www.sciencedirect.com/science/article/pii/S2213158216302248},
	author = {Mohammadian, Mehrbod and Timo Roine and Jussi Hirvonen and Timo Kurki and Henna Ala-Sepp{\"a}l{\"a} and Janek Frantz{\'e}n and Ari Katila and Anna Kyll{\"o}nen and Henna-Riikka Maanp{\"a}{\"a} and Jussi Posti and Riikka Takala and Jussi Tallus and Olli Tenovuo}
}
@article {1761,
	title = {Inline Discrete Tomography system: application to agricultural product inspection},
	journal = {Computers and Electronics in Agriculture},
	volume = {138},
	year = {2017},
	pages = {117{\textendash}126},
	doi = {https://doi.org/10.1016/j.compag.2017.04.010},
	author = {Luis Filipe Alves Pereira and Eline Janssens and George D C Cavalcanti and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1755,
	title = {A Novel adaptive PCA Based Denoising Technique for Ultra-High-Rate Computed Tomography},
	booktitle = {7th Conference on Industrial Computed Tomography (iCT 2017)},
	year = {2017},
	month = {02/2017},
	publisher = {ndt.net},
	organization = {ndt.net},
	address = {Leuven (Belgium)},
	abstract = {Increasing the X-ray tomography acquisition rate is of high importance especially in medical applications as well as dynamic processes investigation. Thanks to the high brilliance of synchrotron radiation, it is possible to reduce the exposure time and do a tomographic scan with a sub-second temporal resolution which allows following dynamic processes in 4D (3D space + time).
On the other hand, increasing the acquisition rate leads to more background noise which strictly limits the advantages of high rate scan. We apply a new fast denoising technique using universal properties of eigen-spectrum of random covariance matrices. Our proposed technique is established based on the principal component analysis (PCA) of redundant data which shows that most of the signal-related variance is contained in a few components, whereas the noise is spread over all components. Extensive numerical evaluations of the proposed technique on a real dataset were acquired at the TOMCAT beamline with its ultra-fast endstation, show significant improvement in the quality of reconstructed images and elimination of noise.},
	keywords = {denoising, eigenvalue, principal component analysis (PCA), ultra-fast scan},
	author = {Karim Zarei Zefreh and Federica Marone Welford and Jan Sijbers},
	editor = {Mehrdad Taki}
}
@article {1641,
	title = {Automatic forensic analysis of automotive paints using optical microscopy},
	journal = {Forensic Science International},
	volume = {259},
	year = {2016},
	pages = {210-220},
	author = {Guy Thoonen and B. Nys and Y. Vander Haegen and G. De Roy and Paul Scheunders}
}
@article {1584,
	title = {Cortical reorganization in an astronaut{\textquoteright}s brain after long-duration spaceflight},
	journal = {Brain Structure and Function},
	volume = {221},
	year = {2016},
	pages = {2873{\textendash}2876},
	doi = {10.1007/s00429-015-1054-3},
	author = {Athena Demertzi and Angelique Van Ombergen and Elena Tomilovskaya and Ben Jeurissen and Ekaterina V. Pechenkova and Carol Di Perri and Liudmila Litvinova and Enrico Amico and Alena Rumshiskaya and Ilya Rukavishnikov and Jan Sijbers and Valentin Sinitsyn and Inessa B. Kozlovskaya and Stefan Sunaert and Paul M Parizel and Paul H Van de Heyning and Steven S L Laureys and Floris L Wuyts}
}
@article {1665,
	title = {Fast inline inspection by neural network based filtered backprojection: Application to apple inspection},
	journal = {Case Studies in Nondestructive Testing and Evaluation},
	volume = {6},
	year = {2016},
	pages = {14{\textendash}20},
	doi = {10.1016/j.csndt.2016.03.003},
	author = {Eline Janssens and Luis Filipe Alves Pereira and Jan De Beenhouwer and Ing Ren Tsang and Mattias Van Dael and Pieter Verboven and Bart Nicolai and Jan Sijbers}
}
@inproceedings {1663,
	title = {Fast X-ray Computed Tomography via Image Completion},
	booktitle = {6th Conference on Industrial Computed Tomography(iCT)},
	year = {2016},
	pages = {1-5},
	address = {Wels, Austria},
	author = {Luis Filipe Alves Pereira and Eline Janssens and Mattias Van Dael and Pieter Verboven and Bart Nicolai and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@inproceedings {1710,
	title = {Pixel clustering for face recognition},
	booktitle = {5th Brazilian Conference on Intelligent Systems},
	year = {2016},
	pages = {121-126},
	address = {Brazil},
	doi = {https://doi.org/10.1109/BRACIS.2016.032},
	author = {Tiago B. A. de Carvalho and Maria A. A. Sibaldo and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@article {1648,
	title = {Unsupervised Retinal Vessel Segmentation Using Combined Filters},
	journal = {Plos One},
	volume = {11},
	number = {2},
	year = {2016},
	pages = {1-21},
	doi = {10.1371/journal.pone.0149943},
	author = {Wendeson S. Oliveira and Joyce Vitor Teixeira and Ing Ren Tsang and George D C Cavalcanti and Jan Sijbers}
}
@conference {1613,
	title = {3D evaluation of clavicle fractures},
	year = {2015},
	author = {Van Tongel, Alexander and Toussaint,, Arnaud and Lieven De Wilde and Toon Huysmans}
}
@article {1570,
	title = {4D-CT reconstruction with unified spatial-temporal patch-based regularization},
	journal = { Inverse Problems and Imaging},
	volume = {9},
	year = {2015},
	pages = {447-467},
	doi = {10.3934/ipi.2015.9.447},
	author = {Kazantsev, Daniil and Thompson, William M. and Lionheart, William R. B. and Van Eyndhoven, Geert and Kaestner,  Anders P. and Katherine J Dobson and Withers, Philip J. and Lee, Peter D.}
}
@article {1634,
	title = {Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder},
	journal = {Molecular Autism},
	volume = {6},
	number = {1},
	year = {2015},
	pages = {65},
	abstract = {Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain{\textquoteright}s wiring diagram, i.e., the connectome.
We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60{\textendash}90 \% of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.
In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.
Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.},
	keywords = {Autism spectrum disorder, Brain networks, Connectivity, Connectome, Diffusion Magnetic Resonance Imaging, Graph theoretical analysis, Tractography, White matter tract},
	issn = {2040-2392},
	doi = {10.1186/s13229-015-0058-4},
	url = {http://dx.doi.org/10.1186/s13229-015-0058-4},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Tani, Pekka and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Caeyenberghs, Karen and Alexander Leemans and Sams, Mikko}
}
@conference {1599,
	title = {Assessing inter-subject variability of white matter response functions used for constrained spherical deconvolution},
	volume = {23},
	year = {2015},
	pages = {2834},
	author = {Ben Jeurissen and Jan Sijbers and Jacques-Donald Tournier}
}
@article {1518,
	title = {Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome},
	journal = {Molecular Autism},
	volume = {6},
	year = {2015},
	pages = {4},
	abstract = {Background: The aim of this study was to investigate potential differences in neural structure in individuals with Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms in AS are severe impairments in social interaction, and restricted or repetitive patterns of behavior, interests or activities. Methods: Diffusion weighted magnetic resonance imaging data was acquired for 14 adult males with AS and 19 age, gender and IQ-matched controls. Voxel-wise group differences in fractional anisotropy (FA) were studied with Tract-Based Spatial
Statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (e.g. crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and the severity of symptoms, we looked for correlations between FA and Autism Spectrum Quotient (AQ), Empathy Quotient and Systemizing Quotient. Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in the temporal part of superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts. However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not explained by the complexity of microstructural organization, measured using the planar diffusion coefficient. In addition, we found a correlation between AQ and FA in the right IFO in the whole group. Conclusions: Our results suggest that there are local and tract-level abnormalities in WM microstructure in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.},
	doi = {10.1186/2040-2392-6-4},
	url = {http://www.molecularautism.com/content/6/1/4},
	author = {Roine, Ulrika and Salmi, Juha and Timo Roine and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Alexander Leemans and Sams, Mikko}
}
@article {1526,
	title = {A geometric unmixing concept for the selection of optimal binary endmember combinations},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {1},
	year = {2015},
	pages = {82-86},
	author = {Laurent Tits and Rob Heylen and Ben Somers and Paul Scheunders and Pol Coppin}
}
@conference {1660,
	title = {Human In Vivo Myeloarchitecture Using Whole-Brain Diffusion MRI},
	year = {2015},
	pages = {1679},
	address = {Toronto, Ontario, Canada},
	author = {Fernando Calamante and Ben Jeurissen and Robert Elton Smith and Jacques-Donald Tournier and Alan Connelly}
}
@conference {1662,
	title = {Time to Move On: An FOD-Based DEC Map to Replace DTI{\textquoteright}s Trademark DEC FA},
	year = {2015},
	pages = {1027},
	author = {Thijs Dhollander and Robert Elton Smith and Jacques-Donald Tournier and Ben Jeurissen and Alan Connelly}
}
@conference {1595,
	title = {Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images},
	volume = {23},
	year = {2015},
	pages = {349},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {1635,
	title = {Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {W. Liao and F. Huang and F. v. Coillie and Guy Thoonen and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
@inproceedings {1533,
	title = {Automated Social behaviour Recognition At Low Resolution},
	booktitle = {ICPR14, International Conference on Pattern Recognition, Stockholm, Sweden},
	year = {2014},
	author = {Tanmay, Nath and G. Liu and Bassem Hassan and Barbara Weyn and Steve De Backer and Paul Scheunders}
}
@conference {1474,
	title = {Brain Tissue Types Resolved Using Spherical Deconvolution of Multi-Shell Diffusion MRI Data},
	year = {2014},
	month = {May},
	pages = {973},
	address = {Milan, Italy},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@inproceedings {1500,
	title = {Conveyor belt X-ray CT using Domain Constrained Discrete Tomography},
	booktitle = {Sibgrapi conference on Graphics, Patterns and Images},
	year = {2014},
	pages = {290 - 297},
	doi = {10.1109/SIBGRAPI.2014.21},
	author = {Luis Filipe Alves Pereira and Andrei Dabravolski and Ing Ren Tsang and George D C Cavalcanti and Jan Sijbers}
}
@article {1494,
	title = {Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data},
	journal = {NeuroImage},
	volume = {103},
	year = {2014},
	pages = {411{\textendash}426},
	abstract = {Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates.

The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the
precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.},
	doi = {10.1016/j.neuroimage.2014.07.061},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@article {1547,
	title = {Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.},
	journal = {NeuroImage},
	volume = {86},
	year = {2014},
	month = {2014 Feb 1},
	pages = {67-80},
	abstract = {There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains "crossing fibers", referring to complex fiber configurations in which multiple and distinctly differently oriented fiber populations exist. Spherical deconvolution based techniques are appealing to characterize this DW intra-voxel signal heterogeneity, as they provide a balanced trade-off between constraints on the required hardware performance and acquisition time on the one hand, and the reliability of the reconstructed fiber orientation distribution function (fODF) on the other hand. Recent findings, however, suggest that an inaccurate calibration of the response function (RF), which represents the DW signal profile of a single fiber orientation, can lead to the detection of spurious fODF peaks which, in turn, can have a severe impact on tractography results. Currently, the computation of this RF is either model-based or estimated from selected voxels that have a fractional anisotropy (FA) value above a predefined threshold. For both approaches, however, there are user-defined settings that affect the RF and, consequently, fODF estimation and tractography. Moreover, these settings still rely on the second-rank diffusion tensor, which may not be the appropriate model, especially at high b-values. In this work, we circumvent these issues for RF calibration by excluding "crossing fibers" voxels in a recursive framework. Our approach is evaluated with simulations and applied to in vivo and ex vivo data sets with different acquisition settings. The results demonstrate that with the proposed method the RF can be calibrated in a robust and automated way without needing to define ad-hoc FA threshold settings. Our framework facilitates the use of spherical deconvolution approaches in data sets in which it is not straightforward to define RF settings a priori.},
	keywords = {Adult, Algorithms, Brain, Calibration, diffusion tensor imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.07.067},
	author = {Chantal M W Tax and Ben Jeurissen and Vos, Sjoerd B. and Viergever, Max A. and Alexander Leemans}
}
@inproceedings {1521,
	title = {Spectral adaptation of hyperspectral flight lines using VHR contextual information},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International},
	year = {2014},
	pages = {2953-2956},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Quebec City, QC, Canada},
	abstract = {Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.},
	doi = {10.1109/IGARSS.2014.6947096},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6947096},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Pieter Kempeneers and Paul Scheunders}
}
@article {1546,
	title = {Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism.},
	journal = {NeuroImage},
	volume = {90},
	year = {2014},
	month = {2014 Apr 15},
	pages = {280-9},
	abstract = {BACKGROUND: The brain-derived neurotrophic factor (BDNF) val66met polymorphism is associated with altered activity dependent secretion of BDNF and a variable influence on brain morphology and cognition. Although a met-dose effect is generally assumed, to date the paucity of met-homozygotes have limited our understanding of the role of the met-allele on brain structure.

METHODS: To investigate this phenomenon, we recruited sixty normal healthy subjects, twenty in each genotypic group (val/val, val/met and met/met). Global and local morphology were assessed using voxel based morphometry and surface reconstruction methods. White matter organisation was also investigated using tract-based spatial statistics and constrained spherical deconvolution tractography.

RESULTS: Morphological analysis revealed an "inverted-U" shaped profile of cortical changes, with val/met heterozygotes most different relative to the two homozygous groups. These results were evident at a global and local level as well as in tractography analysis of white matter fibre bundles.

CONCLUSION: In contrast to our expectations, we found no evidence of a linear met-dose effect on brain structure, rather our results support the view that the heterozygotic BDNF val66met genotype is associated with cortical morphology that is more distinct from the BDNF val66met homozygotes. These results may prove significant in furthering our understanding of the role of the BDNF met-allele in disorders such as Alzheimer{\textquoteright}s disease and depression.},
	keywords = {Adolescent, Adult, Alleles, Brain, Brain-Derived Neurotrophic Factor, diffusion tensor imaging, Female, Genotype, Heterozygote, Homozygote, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Polymorphism, Single Nucleotide, Young Adult},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.12.050},
	author = {Forde, Natalie J and Ronan, Lisa and Suckling, John and Scanlon, Cathy and Neary, Simon and Holleran, Laurena and Alexander Leemans and Tait, Roger and Rua, Catarina and Fletcher, Paul C and Ben Jeurissen and Dodds, Chris M and Miller, Sam R and Bullmore, Edward T and Colm McDonald and Nathan, Pradeep J and Dara M. Cannon}
}
@inproceedings {1478,
	title = {Type-2 fuzzy GMMs for robust text-independent speaker verification in noisy environments},
	booktitle = {International Conference of Pattern Recognition},
	year = {2014},
	author = {Hector N. B. Pinheiro and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@article {1317,
	title = {Classification of heathland vegetation in a hierarchical contextual framework},
	journal = {International Journal of Remote Sensing},
	volume = {34},
	year = {2013},
	month = {2013},
	pages = {96 - 111},
	abstract = {Heathlands in Western Europe have shown dramatic declines over the last century and therefore have been given a high conservation priority in the Habitats Directive of the European Union (EU). Accurate surveying and monitoring of heathland habitats is essential for appropriate conservation management, but the large heterogeneity of vegetation types within habitats as well as the occurrence of similar vegetation across habitat types hinders a straightforward, automated mapping based on aerial images. In such a case, a context-dependent classification algorithm is expected to be superior to traditional classification techniques. This article presents a novel approach to map the conservation status of heathland vegetation by using a hierarchical classification scheme that describes the structural dependencies in the field between the basic vegetation and the land-cover types that habitats are composed of. These dependency relationships are included as contextual information in the classification process, using a tree-structured Markov random field (TS-MRF) technique with a tree that reflects the hierarchy of the classification scheme. Results of this approach for a heathland area in Belgium were compared with results from more conventional classification approaches. Validation of the results showed that the structure of the scheme contained important spatial relationships, which were further reinforced by using the contextual classification strategy, especially for the most detailed level of the classification scheme. Accuracy increased and the classification results were more suitable for visual interpretation.},
	issn = {0143-1161},
	doi = {10.1080/01431161.2012.708061},
	author = {Guy Thoonen and Toon Spanhove and Jeroen Vanden Borre and Paul Scheunders}
}
@article {1379,
	title = {Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	month = {2013},
	pages = {779 - 791},
	abstract = {This paper describes a hyperspectral image classification method to obtain classification maps at a finer resolution than the image{\textquoteright}s original resolution. We assume that a complementary color image of high spatial resolution is available. The proposed methodology consists of a soft classification procedure to obtain landcover fractions, followed by a subpixel mapping of these fractions. While the main contribution of this article is in fact the complete multisource framework for obtaining a subpixel map, the major novelty of this subpixel mapping approach is the inclusion of contextual information, obtained from the color image. Experiments, conducted on two hyperspectral images and one real multi source data set, show excellent results, when compared to classification of the hyperspectral data only. The advantage of the contextual approach, compared to conventional subpixel mapping approaches, is clearly demonstrated.},
	keywords = {fusion, hyperspectral data, spectral unmixing, subpixel mapping, superresolution},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2012.2236539},
	author = {Zahid Mahmood and Muhamed Awais Akhter and Guy Thoonen and Paul Scheunders}
}
@inproceedings {Jacobs13,
	title = {Domain adaptation with Hidden Markov Random Fields},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International},
	year = {2013},
	month = {July},
	pages = {3112-3115},
	address = {Melbourne, VIC, Australia},
	abstract = {In this paper, we propose a method to match multitemporal sequences of hyperspectral images using Hidden Markov Random Fields. Based on the matching of the data manifold, the algorithm matches the reflectance spectra of the classes, thus allowing the reuse of labeled examples acquired on one image to classify the other. This allows valorization of spectra collected in situ to other acquisitions than the one they were acquired for, without user supervision, prior knowledge of the class reflectance in the new domain or global information about atmospheric conditions.},
	keywords = {domain adaptation, graph matching, Hidden Markov Random Fields, Multitemporal classification},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2013.6723485},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Birgen Haest and Paul Scheunders}
}
@inproceedings {1524,
	title = {Improving the efficiency of MESMA through geometric unmixing principles},
	booktitle = {SPIE Remote Sensing, Drseden, Germany, 23-26 September },
	volume = {8892},
	number = {UNSP 88920Q},
	year = {2013},
	author = {Laurent Tits and Ben Somers and Rob Heylen and Paul Scheunders and Pol Coppin}
}
@article {1412,
	title = {Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: A diffusion tensor imaging study},
	journal = {Autism Research},
	volume = {6},
	year = {2013},
	pages = {642-650},
	chapter = {642},
	doi = {10.1002/aur.1332},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Alexander Leemans and Sams, Mikko}
}
@article {1348,
	title = {Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.},
	journal = {Human Brain Mapping},
	volume = {34},
	number = {11},
	year = {2013},
	pages = {2747-66},
	abstract = {It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be \~{}90\% (using CSD) and 63\% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.},
	issn = {1097-0193},
	doi = {10.1002/hbm.22099},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@inproceedings {Sabino:2013:MCT:2571272.2572228,
	title = {Motion Compensation Techniques in Permutation-Based Video Encryption},
	booktitle = {Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics},
	series = {SMC {\textquoteright}13},
	year = {2013},
	pages = {1578{\textendash}1581},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Washington, DC, USA},
	keywords = {motion compensation, multimedia data security, spatial correlation, video coding, Video encryption},
	isbn = {978-1-4799-0652-9},
	doi = {10.1109/SMC.2013.272},
	url = {http://dx.doi.org/10.1109/SMC.2013.272},
	author = {Sabino, Caio C. and Andrade, Lais S. and Ing Ren Tsang and George D C Cavalcanti and Ing Jyh Tsang and Jan Sijbers}
}
@conference {1557,
	title = {Recursive calibration of the fiber response function for spherical deconvolution diffusion ODF sharpening},
	year = {2013},
	address = {Podstrana, Croatia},
	author = {Chantal M W Tax and Ben Jeurissen and Vos, Sjoerd B. and Viergever, Max A. and Alexander Leemans}
}
@conference {1556,
	title = {Robust fiber response function estimation for deconvolution based diffusion MRI methods},
	volume = {21},
	year = {2013},
	pages = {3149},
	address = {Salt Lake City, Utah},
	author = {Chantal M W Tax and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@conference {1555,
	title = {Robust fiber response function estimation for deconvolution based diffusion MRI methods},
	volume = {5},
	year = {2013},
	pages = {55},
	address = {Rotterdam, The Netherlands},
	author = {Chantal M W Tax and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@article {ThoonenHufkensBorreSpanhovepscheund2011,
	title = {Accuracy assessment of contextual classification results for vegetation mapping},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	volume = {15},
	year = {2012},
	pages = {7 - 15},
	abstract = {A new procedure for quantitatively assessing the geometric accuracy of thematic maps, obtained from classifying hyperspectral remote sensing data, is presented. More specifically, the methodology is aimed at the comparison between results from any of the currently popular contextual classification strategies. The proposed procedure characterises the shapes of all objects in a classified image by defining an appropriate reference and a new quality measure. The results from the proposed procedure are represented in an intuitive way, by means of an error matrix, analogous to the confusion matrix used in traditional thematic accuracy representation. A suitable application for the methodology is vegetation mapping, where lots of closely related and spatially connected land cover types are to be distinguished. Consequently, the procedure is tested on a heathland vegetation mapping problem, related to Natura 2000 habitat monitoring. Object-based mapping and Markov Random Field classification results are compared, showing that the selected Markov Random Fields approach is more suitable for the fine-scale problem at hand, which is confirmed by the proposed procedure.},
	keywords = {Accuracy assessment, Confusion matrix, Contextual classification, Natura 2000},
	issn = {0303-2434},
	doi = {10.1016/j.jag.2011.05.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0303243411000766},
	author = {Guy Thoonen and Koen Hufkens and Jeroen Vanden Borre and Toon Spanhove and Paul Scheunders}
}
@conference {1341,
	title = {Assessing the implications of complex fiber configurations for DTI metrics in real data sets},
	year = {2012},
	month = {May},
	pages = {3584},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@inproceedings {1352,
	title = {Automatic threshold selection for morphological attribute profiles},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {4946-4949},
	author = {Zahid Mahmood and Guy Thoonen and Paul Scheunders}
}
@mastersthesis {1333,
	title = {Contextual classification of hyperspectral remote sensing images - Application in vegetation monitoring},
	year = {2012},
	pages = {156},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Guy Thoonen}
}
@conference {1415,
	title = {DTI-based Classification of Affection Status in Asperger Syndrome.},
	year = {2012},
	author = {Roine, Ulrika and Timo Roine and Salmi, Juha and Nieminen-von Wendt, Taina and Lepp{\"a}m{\"a}ki, Sami and Rintahaka, Pertti and Tani, Pekka and Sams, Mikko}
}
@article {1515,
	title = {Extraction of airways from CT (EXACT{\textquoteright}09).},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {31},
	year = {2012},
	month = {2012 Nov},
	pages = {2093-107},
	abstract = {This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74\% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.},
	keywords = {Algorithms, Analysis of Variance, Databases, Factual, Humans, Lung, Radiographic Image Enhancement, Tomography, X-Ray Computed, Trachea},
	issn = {1558-254X},
	doi = {10.1109/TMI.2012.2209674},
	author = {Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and de Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and R{\^o}mulo Pinho and Jan Sijbers and Feuerstein, Marco and Fabija{\'n}ska, Anna and Bauer, Christian and Beichel, Reinhard and Mendoza, Carlos S and Wiemker, Rafael and Lee, Jaesung and Reeves, Anthony P and Born, Silvia and Weinheimer, Oliver and van Rikxoort, Eva M and Tschirren, Juerg and Mori, Ken and Odry, Benjamin and Naidich, David P and Hartmann, Ieneke and Eric A. Hoffman and Prokop, Mathias and Pedersen, Jesper H and de Bruijne, Marleen}
}
@conference {1340,
	title = {HARDI-based methods for fiber orientation estimation},
	year = {2012},
	note = {Received the Summa Cum Laude Merit Award for the 20th Annual ISMRM meeting.},
	month = {May},
	pages = {3585},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@mastersthesis {1337,
	title = {Improved analysis of brain connectivity using high angular resolution diffusion MRI},
	volume = {PhD in Sciences},
	year = {2012},
	month = {03/2012},
	pages = {208},
	school = {University of Antwerp},
	type = {PhD thesis},
	keywords = {complex WM architecture, constrained spherical deconvolution, crossing fibers, CSD, Diffusion MRI, diffusion tensor imaging, DTI, HARDI, high angular resolution diffusion imaging, Magnetic Resonance Imaging, MRI, probabilistic tractography, Spherical deconvolution, Tractography, white matter, WM},
	author = {Ben Jeurissen}
}
@article {ThoonenMahmoodPeeterspscheund2012,
	title = {Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion},
	journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
	volume = {5},
	number = {2},
	year = {2012},
	pages = {510 - 521},
	abstract = {In this work, we treat the problem of combined classification of a high spatial resolution color image and a lower spatial resolution hyperspectral image of the same scene. The problem is particularly challenging, since we aim for classification maps at the spatial resolution of the color image. Contextual information is obtained from the color image by introducing Color Attribute Profiles (CAPs). Instead of treating the {\textquoteleft}R{\textquoteright}, {\textquoteleft}G{\textquoteright}, and {\textquoteleft}B{\textquoteright} bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, which corresponds to the Luminance, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands, which contain the color information, are kept intact, and the resulting images are transformed back into RGB space. The spectral information is obtained from the hyperspectral image. A Composite Decision Fusion (CDF) strategy is proposed, combining a state-of-the-art kernel-based decision fusion technique with the popular composite kernel classification approach. Experiments are conducted, using simulated data and a real multisource dataset containing airborne hyperspectral data and orthophotographic data from a suburban area in Belgium. These experiments show that our CAPs perform well with respect to other approaches for extracting attribute profiles from high resolution color images, and that the proposed CDF strategy produces meaningful results with respect to concatenation and the highlighted state-of-the-art approaches for combining multisource data.},
	keywords = {Color, image classification, morphological operations, multiresolution techniques, multisensor systems},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2011.2168317},
	author = {Guy Thoonen and Zahid Mahmood and Stijn Peeters and Paul Scheunders}
}
@inproceedings {1507,
	title = {Subpixel mapping of hyperspectral data using high resolution color images},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Zahid Mahmood and Guy Thoonen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {rpinhoTournoyjsijbers2011,
	title = {Assessment and Stenting of Tracheal Stenosis using Deformable Shape Models},
	journal = {Medical Image Analysis},
	volume = {15},
	number = {2},
	year = {2011},
	pages = {250-266},
	abstract = {This work presents a decision support system for the assessment of tracheal stenosis. In the proposed method, a statistical shape model of healthy tracheas is registered to a 3D CT image of a patient with tracheal stenosis. The registration yields an estimation of the shape of the patient{\textquoteright}s trachea as if stenosis was not present. From this point, the extent and the severity of the stenosis is assessed and stent parameters are obtained automatically. The method was extensively evaluated on simulation as well on real data and the results showed that it is accurate and fast enough to be used in the clinical setting.},
	issn = {1361-8423},
	doi = {http://dx.doi.org/doi:10.1016/j.media.2010.12.001},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers}
}
@inproceedings {WitteThoonenpscheundPizuricaPhilips2011,
	title = {Classification of multi-source images using color mathematical morphological profiles},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International},
	year = {2011},
	pages = {3919 - 3922},
	address = {Vancouver, BC, Canada},
	abstract = {In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.},
	keywords = {Classification, Color, Morphological profiles, Multisource images, remote sensing},
	isbn = {978-1-4577-1003-2},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2011.6050088},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6050088\&isnumber=6048881},
	author = {Val{\'e}rie De Witte and Guy Thoonen and Paul Scheunders and Aleksandra Pizurica and Wilfried Philips}
}
@inproceedings {MahmoodWittepscheund2011,
	title = {Multi-source image classification using color attribute profiles},
	booktitle = {3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2011},
	pages = {1 - 4},
	address = {Lisbon, Portugal},
	abstract = {This work introduces a method to extract attribute profiles from RGB color images with high spatial resolution, for instance images acquired from Unmanned Aerial Vehicles (UAV). The resulting Color Attribute Profiles (CAP) are intended to improve the classification of low spatial resolution hyperspectral images by merging the attribute features with the spectral features of the hyperspectral image. Instead of treating the R, G and B bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands are kept intact, and the resulting images are transformed back into RGB space. In our experiments, classification results using this methodology are compared to classification results using other strategies for extracting attribute profiles in CIE-Lab space, as well as regular grayscale attribute profiles.},
	keywords = {Attributes, Classification, Color, Hyperspectral, Morphology},
	isbn = {978-1-4577-2202-8},
	issn = {2158-6268},
	doi = {10.1109/WHISPERS.2011.6080859},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6080859\&isnumber=6080842},
	author = {Zahid Mahmood and Guy Thoonen and Val{\'e}rie De Witte and Paul Scheunders}
}
@article {1258,
	title = {Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution},
	journal = {Human Brain Mapping},
	volume = {32},
	year = {2011},
	month = {March, 2011},
	pages = {461 - 479},
	keywords = {Diffusion MRI, Spherical deconvolution, Tractography},
	doi = {10.1002/hbm.21032},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@article {P.M.A.S.bjeurissJ.A.M.K.F.T.J.-F.C.2011,
	title = {Quantitative Evaluation of 10 Tractography Algorithms on a Realistic Diffusion MR Phantom},
	journal = {NeuroImage},
	volume = {56},
	number = {1},
	year = {2011},
	month = {May},
	pages = {220-234},
	author = {Pierre Fillard and Maxime Descoteaux and Alvina Goh and Sylvain Gouttard and Ben Jeurissen and James Malcolm and Alonso Ramirez-Manzanares and Marco Reisert and Ken Sakaie and Fatima Tensaouti and Ting-Shou Yo and Jean-Fran{\c c}ois Mangin and Cyril Poupon}
}
@article {1267,
	title = {Analysis of micro computed tomography images; a look inside historic enamelled metal objects},
	journal = {Applied Physics A},
	volume = {98},
	year = {2010},
	month = {2/2010},
	pages = {385 - 392},
	issn = {0947-8396},
	doi = {10.1007/s00339-009-5394-9},
	author = {Annemie Van Der Linden and Elke Van de Casteele and Thomas, Mienke Simon and Vos, Annemie and Janssen, Elsje and Janssens, Koen}
}
@inbook {rpinhoTournoyjsijbers2010,
	title = {Computer-Aided Assessment and Stenting of Tracheal Stenosis},
	booktitle = {Computer Aided Diagnosis of Lung Imaging},
	year = {2010},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers},
	editor = {Ayman El-Baz}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Counting the number of fiber orientations in diffusion MRI voxels using constrained spherical deconvolution},
	year = {2010},
	month = {January},
	address = {Utrecht, The Netherlands},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2010,
	title = {A Decision Support System for the Treatment of Tracheal Stenosis},
	booktitle = {Proc. of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	month = {August},
	pages = {72-76},
	publisher = {IAPR},
	organization = {IAPR},
	address = {Istanbul},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Estimating the number of fiber orientations in diffusion MRI voxels: a constrained spherical deconvolution study},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {Thoonen10,
	title = {Habitat mapping and quality assessment of heathlands using a modified kernel-based reclassification technique},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International},
	year = {2010},
	month = {July},
	pages = {2707 - 2710},
	address = {Honolulu, HI, USA},
	abstract = {This article presents a method for acquiring habitat maps, intended for monitoring and evaluating the conservation status of heathland vegetation, starting from thematic land cover maps. The procedure is a modified kernel-based reclassification technique, that fits into a complete habitat quality assessment framework. Part one of the procedure shifts a small square kernel over the land cover map and assigns a habitat type to each position that complies with a single set of expert rules, related to the land cover composition in that position. Part two fills the gaps, by assigning a habitat type to any of the map positions that don{\textquoteright}t conform to any of the rules, or to more than one set of rules, by using a distance measure. The technique is tested on real data from a heathland site and shows some promising results.},
	keywords = {Belgium, environmental factors, geophysical image processing, geophysical techniques, habitat mapping, heathland vegetation, image classification, Kalmthoutse Heide, kernel-based reclassification technique, quality assessment, terrain mapping, thematic land cover maps, vegetation mapping},
	isbn = {978-1-4244-9565-8},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2010.5649240},
	author = {Guy Thoonen and Toon Spanhove and Haest, B. and Jeroen Vanden Borre and Paul Scheunders}
}
@article {Hufkens10,
	title = {Habitat reporting of a heathland site: Classification probabilities as additional information, a case study},
	journal = {Ecological Informatics},
	volume = {5},
	number = {4},
	year = {2010},
	pages = {248 - 255},
	abstract = {Heathlands are man-made habitats and their decline during the last century can be contributed to shifts in both agricultural and management practices as well as to hydrological and atmospheric changes. As a result, many heathland sites, including the Kalmthoutse Heide in Belgium, were included in the European Natura 2000 program, a network of protected areas across the European Union. To assure an accurate mapping of the Kalmthoutse Heide and other Natura 2000 sites in Belgium a classification framework for habitat status reporting with remote sensing data and in particular high resolution hyperspectral imagery was started. In this study we propose a simple and fast context based method for mapping heathland heterogeneity using the intermediate, otherwise redundant, classification probabilities as generated by a hard classification algorithm. Our study proved to be successful in using intermediate classification probabilities as a valuable source of ecological information. The delineated areas have been shown to be statistically sound and robust compared to a neutral model. The technique is not limited to a particular hard classification technique and can easily be adopted into current vegetation monitoring efforts. The resulting maps provided accessible maps which can support management of the protected site and enhance the accuracy of EU reportage as required by the habitat directive.},
	keywords = {Heterogeneity, Hyperspectral, Patch, remote sensing, Uncertainty, Vegetation},
	issn = {1574-9541},
	doi = {10.1016/j.ecoinf.2009.09.002},
	url = {http://www.sciencedirect.com/science/article/pii/S1574954109000740},
	author = {Koen Hufkens and Guy Thoonen and Jeroen Vanden Borre and Paul Scheunders and Reinhart Ceulemans}
}
@inproceedings {Haest10,
	title = {An object-based approach to quantity and quality assessment of heathland habitats in the framework of NATURA 2000 using hyperspectral airborne AHS images.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Wards clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93\% (Kappa= 0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodologys potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.},
	keywords = {Application, Classification, Contextual, Ecosystem, Hyper spectral, Landscape, Object, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Haest_211_An_object-based_approach_to_quantity_and_quality_assessment_of_heath_land_habitats.pdf},
	author = {Haest, B. and Guy Thoonen and Jeroen Vanden Borre and Toon Spanhove and Stephanie Delalieux and L. Bertels and Kooistra, L. and C A M{\"u}cher and Paul Scheunders}
}
@inproceedings {Thoonen10b,
	title = {Using patch metrics as validation for contextual classification of heathland vegetation.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {This article presents a method to assess the accuracy of vegetation maps for which contextual information has been included in the classification process. It is well known that land use classification may benefit from combining spatial and spectral information. Consequently, many classification techniques incorporating spatial information have been implemented. To compare various contextual classification techniques, the shape of vegetation patches, in the spatially enhanced maps, are statistically linked to their counterparts in the spectral classification result, on which these spatial enhancements are applied. To this end, measures for the change in shape of patches are introduced. The shape of any patch is characterized by the edges between the patch and its neighbors. Therefore, patch shape can be represented by an edge map in which each pixel gets the value of the number of classes that are different from the class label of the central pixel in a four-adjacency neighborhood. Rather than defining a single metric for the edge map difference, an error matrix is used to depict not only how many edges have changed with respect to the reference, but also by how much they have changed. The method is tested on contextual classification results of heathland vegetation.},
	keywords = {Accuracy, Classification, Contextual, Hierarchical, Hyper spectral, Quality, Spatial, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Thoonen_44_UsingPatchMetricsAsValidationForContextualClassificationOfHeathlandVegetation.pdf},
	author = {Guy Thoonen and Koen Hufkens and Jeroen Vanden Borre and Paul Scheunders}
}
@inproceedings {Thoonen09,
	title = {Assessing the quality of heathland vegetation by classification of hyperspectral data using spatial information},
	booktitle = {Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009},
	volume = {4},
	year = {2009},
	month = {July},
	pages = {IV-330 - IV-333},
	address = {Cape Town, South Africa},
	abstract = {This article deals with a method for acquiring vegetation maps, suitable for monitoring and evaluating the conservation status of heathland vegetation from hyperspectral data. The applied method is a recursive supervised segmentation algorithm based on a Tree-structured Markov Random Field (TS-MRF), capable of incorporating structural dependencies in the classification process. To this end, a tree structure is used that is built upon structural dependencies that are present in the field. The classification results from this TS-MRF with extended tree are compared to pixel-based classification results, results from a simple smoothing post-processing, and the result from the original binary TS-MRF technique.},
	keywords = {geophysical signal processing, heathland vegetation conservation, heathland vegetation quality, hyperspectral data classification, image segmentation, Markov processes, pixel based classification comparison, recursive supervised segmentation algorithm, smoothing post processing comparison, spatial information, tree structured Markov random field, trees (mathematics), TS-MRF, vegetation mapping, vegetation maps},
	isbn = {978-1-4244-3394-0},
	doi = {10.1109/IGARSS.2009.5417380},
	author = {Guy Thoonen and Jeroen Vanden Borre and Steve De Backer and Paul Scheunders}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2009,
	title = {Assessment of Tracheal Stenosis Using Active Shape Models of Healthy Tracheas: A Surface Registration Study},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {125-136},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2009,
	title = {Fiber Tracking on the {\textquoteright}Fiber Cup Phantom{\textquoteright} using Constrained Spherical Deconvolution},
	booktitle = {MICCAI workshop on Diffusion Modelling and the Fiber Cup (DMFC{\textquoteright}09)},
	year = {2009},
	address = {London, United Kingdom},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2009,
	title = {Probabilistic Fiber Tracking using the Residual Bootstrap with Constrained Spherical Deconvolution MRI},
	year = {2009},
	month = {April},
	address = {Honolulu, USA},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {Driesen09,
	title = {Spatial hyperspectral image classification by prior segmentation},
	booktitle = {Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009},
	volume = {3},
	year = {2009},
	month = {July},
	pages = {III-709 - III-712},
	address = {Cape Town, South Africa},
	abstract = {In this paper, we propose a technique to incorporate spatial features in the classification of hyperspectral data by means of a prior segmentation of the dataset. The key idea of the technique is that each pixel is not classified individually, but that the regions obtained from the prior segmentation are classified as a whole. The proposed technique is validated on a hyperspectral dataset of a heathland area in Belgium. Experimental results show that we can achieve larger and spatially smoothed regions, while the overall classification success rate is comparable to the pure spectral classification results.},
	keywords = {Belgium, geophysical image processing, heathland area, hyperspectral data classification, image classification, image segmentation, prior segmentation, remote sensing, spatial hyperspectral image classification, spatially smoothed regions, spectral classification},
	isbn = {978-1-4244-3394-0},
	doi = {10.1109/IGARSS.2009.5417861},
	author = {J. Driesen and Guy Thoonen and Paul Scheunders}
}
@inproceedings {Zhangsdbackerpscheund2008,
	title = {Bayesian fusion of multispectral and hyperspectral image in wavelet domain},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, Boston, United States, 6-11 July},
	year = {2008},
	pages = {69-72},
	publisher = {IEEE International},
	organization = {IEEE International},
	author = {Y. Zhang and Steve De Backer and Paul Scheunders},
	editor = {t.b.a.}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Bootstrap methods for estimating uncertainty in Constrained Spherical Deconvolution fiber orientations},
	year = {2008},
	month = {May},
	pages = {3324},
	address = {Toronto, Canada},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Can residual bootstrap reliably estimate uncertainty in fiber orientation obtained by spherical deconvolution from diffusion-weighted MRI?},
	volume = {41},
	year = {2008},
	month = {June},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2008,
	title = {Estimation of Uncertainty in Constrained Spherical Deconvolution Fiber Orientations},
	booktitle = {5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2008},
	pages = {907-910},
	address = {Paris, France},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {Thoonen08,
	title = {Spatial Classification of Hyperspectral Data of Dune Vegetation along the Belgian Coast},
	booktitle = {Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International},
	volume = {3},
	year = {2008},
	month = {July},
	pages = {III-483 - III-486},
	address = {Boston, MA, USA},
	abstract = {This work evaluates a classification method, including spatial information, for dune vegetation along the Belgian coastline. The used method is a recursive supervised segmentation algorithm based on a tree-structured Markov Random Field. This technique describes a K-ary field as a sequence of binary Markov Random Fields, each of which is represented by a node in the tree. The obtained classification results were compared to results with the same data set, for a purely spectral classification and a spectral classification, followed by spatial smoothing.},
	keywords = {airborne data, airborne radar, Belgian Coast, binary Markov Random Field, dune environment, dune vegetation mapping, Europe, hyperspectral data, image classification, image segmentation, Markov processes, spatial classification, spatial smoothing, supervised segmentation algorithm, tree-structured Markov Random Field, trees (mathematics), TS-MRF model, vegetation mapping},
	isbn = {978-1-4244-2807-6},
	doi = {10.1109/IGARSS.2008.4779389},
	author = {Guy Thoonen and Steve De Backer and S. Provoost and Pieter Kempeneers and Paul Scheunders}
}
@inproceedings {aduijstesdbackerpscheund2007,
	title = {Wavelet-based Multicomponent Image Restoration},
	booktitle = {Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, United States, September 9-12},
	series = {Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference},
	volume = {6763},
	year = {2007},
	month = {October},
	author = {A. Duijster and Steve De Backer and Paul Scheunders},
	editor = {F. Truchetet and O. Laligant}
}
@article {GroofmverhoyeMeirTindemansaleemansavdlinde2006,
	title = {In vivo diffusion tensor imaging (DTI) of brain subdivisions and vocal pathways in songbirds},
	journal = {NeuroImage},
	volume = {29},
	year = {2006},
	pages = {754-763},
	author = {G. De Groof and Marleen Verhoye and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@mastersthesis {1272,
	title = {Reconstructie van Transversaal Getrunceerde Cone Beam Projecties in Micro-Tomografie},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {G. Tisson}
}
@inproceedings {KempeneerssdbackerTejadaDelalieuxCantoIribasAardtCoppinpscheund2006,
	title = {Stress detection in orchards with hyperspectral remote sensing data},
	booktitle = {SPIE Remote Sensing, Stockholm, Sweden, september 11-14},
	volume = {6359},
	number = {38},
	year = {2006},
	month = {September},
	author = {Pieter Kempeneers and Steve De Backer and P J Zarco Tejada and Stephanie Delalieux and G Sepulcre Canto and F Morales Iribas and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@inproceedings {GroofmverhoyeMeirTindemansaleemansavdlinde2005,
	title = {In Vivo Visualization of the Neuroanatomy and Brain Connectivity of Starling Brain Through Diffusion Tensor Imaging},
	booktitle = {6th Bi-Annual Meeting {\textendash} Belgian Society for Neuroscience},
	year = {2005},
	address = {Brussels, Belgium},
	author = {G. De Groof and Marleen Verhoye and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {gtissonpscheunddvandyck2004,
	title = {3D region of interest X-ray CT for geometric magnification from multiresolution acquisitions},
	booktitle = {Proc. ISBI04, IEEE International Symposium on Biomedical Imaging, 15-18 april 2004, Arlington, VA},
	year = {2004},
	pages = {567-570},
	author = {G. Tisson and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {mverhoyeGroofMeirTindemansaleemansavdlinde2004,
	title = {In vivo neuroanatomy of the songbird brain, visualized through diffusion tensor imaging},
	booktitle = {21th Annual Scientific Meeting - European Society for Magnetic Resonance in Medicine and Biology},
	year = {2004},
	pages = {161},
	address = {Copenhagen, Denmark},
	author = {Marleen Verhoye and G. De Groof and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {mverhoyeGroofMeirTindemansaleemansavdlinde2004,
	title = {In vivo visualization of the neuroanatomy and brain connectivity of starling brain through diffusion tensor imaging},
	booktitle = {34rd Annual Meeting of the Society for Neuroscience},
	year = {2004},
	address = {San Diego, USA},
	author = {Marleen Verhoye and G. De Groof and V. Van Meir and I. Tindemans and Alexander Leemans and Annemie Van Der Linden}
}
@inproceedings {gvgompeldvandyckgtissonjsijbers2004,
	title = {A new algorithm for Region Of Interest Tomography},
	booktitle = {19th Annual Symposium of the Belgian Hospital Physicists Association},
	year = {2004},
	month = {January},
	author = {Gert Van Gompel and Dirk Van Dyck and G. Tisson and Jan Sijbers}
}
@inproceedings {gvgompelgtissondvandyckjsijbers2004,
	title = {A new algorithm for Region Of Interest Tomography},
	booktitle = {SPIE Medical Imaging: Image Processing},
	volume = {127},
	year = {2004},
	month = {February},
	pages = {2105-2113},
	address = {San Diego, California, USA},
	doi = {10.1117/12.536685},
	author = {Gert Van Gompel and G. Tisson and Dirk Van Dyck and Jan Sijbers},
	editor = {Milan Sonka}
}
@inproceedings {gtissonpscheunddvandyck2004,
	title = {ROI Cone-Beam CT on a Circular Orbit for Geometric Magnification Using Reprojection},
	booktitle = {Proc. IEEE Medical Imaging Conference, Rome, 16-22 october},
	year = {2004},
	author = {G. Tisson and Paul Scheunders and Dirk Van Dyck}
}
@mastersthesis {1285,
	title = {On the wavefront aberrations of the human eye and the search for their origins},
	year = {2004},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Jos Rozema}
}
@mastersthesis {1288,
	title = {Pattern Recognition and Complex Systems},
	year = {2000},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Ing Ren Tsang}
}
@mastersthesis {1287,
	title = {Pattern Recognition, Neighborhood Codes, and Lattice Animals},
	year = {2000},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Ing Jyh Tsang}
}
@article {ijtsangirtsangbdboeckdvandyck2000,
	title = {Scaling and critical probability for cluster size and lattice animals diversity on randomly occupied square lattices},
	journal = {Journal of Physics A: Mathematical and General},
	volume = {33},
	number = {14},
	year = {2000},
	pages = {2739-2754},
	author = {Ing Jyh Tsang and Ing Ren Tsang and Bart De Boeck and Dirk Van Dyck}
}
@article {WeynTjalamgvdwouweDaelepscheundJacobMarckdvandyck2000,
	title = {Validation of nuclear texture density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma},
	journal = {Analytical and Quantitative Cytology and Histology},
	volume = {22},
	number = {5},
	year = {2000},
	pages = {373-382},
	author = {Barbara Weyn and W. Tjalam and G. Van de Wouwer and A. Van Daele and Paul Scheunders and W. Jacob and E. Van Marck and Dirk Van Dyck}
}
@article {irtsangijtsang1999,
	title = {Cluster size diversity, percolation, and complex systems},
	journal = {Physical Review E},
	volume = {60},
	number = {3},
	year = {1999},
	pages = {2684-2698},
	author = {Ing Ren Tsang and Ing Jyh Tsang}
}
@article {ijtsangirtsangdvandyck1999,
	title = {Image processing using neighbourhood coding},
	journal = {Pattern Recognition Letters},
	volume = {20},
	year = {1999},
	pages = {1279-1286},
	author = {Ing Jyh Tsang and Ing Ren Tsang and Dirk Van Dyck}
}
@inproceedings {irtsangijtsangpscheunddvandyck1998,
	title = {Pattern Recognition using neighborhood coding},
	booktitle = {Proc. CVPRIP{\textquoteright}98, Intern. Workshop on Computer Vision, Pattern Recognition and Image Processing , North Carolina, october 23-28},
	year = {1998},
	pages = {250-253},
	author = {Ing Ren Tsang and Ing Jyh Tsang and Paul Scheunders and Dirk Van Dyck}
}
