@article {2383,
	title = {Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI},
	journal = {Journal of Nondestructive Evaluation},
	volume = {44},
	year = {2025},
	doi = {https://doi.org/10.1007/s10921-024-01147-9},
	author = {Miroslav Yosifov and Thomas Lang and Virginia Florian and Stefan Gerth and Jan De Beenhouwer and Sijbers, Jan and Johann Kastner and Christoph Heinzl}
}
@inproceedings {2293,
	title = {Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations},
	booktitle = {e-Journal of Nondestructive Testing},
	volume = {28},
	year = {2023},
	issn = {1435-4934 },
	doi = {doi.org/10.58286/27716},
	author = {Miroslav Yosifov and Patrick Weinberger and Michael Reiter and Bernhard Fr{\"o}hler and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@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 {2246,
	title = {Probability of Detection applied to X-ray inspection using numerical simulations},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	pages = {536-551},
	doi = {10.1080/10589759.2022.2071892},
	author = {Miroslav Yosifov and M. Reiter and S. Heupl and C. Gusenbauer and Bernhard Fr{\"o}hler and R. Fernandez- Gutierrez and Jan De Beenhouwer and Jan Sijbers and Johann Kastner and Christoph Heinzl}
}
@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}
}
@article {1903,
	title = {A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila},
	journal = {Plos Computational Biology},
	volume = {14},
	year = {2018},
	pages = {e1006410},
	author = {G Liu and Tanmay Nath and Z Guo and G Linneweber and A Claeys and J Li and M Bengochea and Steve De Backer and Barbara Weyn and M Sneyders and H Nicasy and P Yu and Paul Scheunders and Bassem Hassan}
}
@article {2039,
	title = {MEKS: A program for computation of inclusive jet cross sections at hadron colliders},
	journal = {Computer Physics Communications},
	volume = {184},
	year = {2013},
	month = {Jan-06-2013},
	pages = {1626 - 1642},
	abstract = {EKS is a numerical program that predicts differential cross sections for production of single-inclusive hadronic jets and jet pairs at next-to-leading order (NLO) accuracy in a perturbative QCD calculation. We describe MEKS 1.0, an upgraded EKS program with increased numerical precision, suitable for comparisons to the latest experimental data from the Large Hadron Collider and Tevatron. The program integrates the regularized patron-level matrix elements over the kinematical phase space for production of two and three partons using the VEGAS algorithm. It stores the generated weighted events in finely binned two-dimensional histograms for fast offline analysis. A user interface allows one to customize computation of inclusive jet observables. Results of a benchmark comparison of the MEKS program and the commonly used FastNLO program are also documented.},
	issn = {00104655},
	doi = {10.1016/j.cpc.2013.01.022},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010465513000611https://api.elsevier.com/content/article/PII:S0010465513000611?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0010465513000611?httpAccept=text/plain},
	author = {Gao, Jun and Liang, Zhihua and Soper, Davison E. and Lai, Hung-Liang and Nadolsky, Pavel M. and Yuan, C.-P.}
}
@article {1413,
	title = {Reelin Associated With Restricted and Stereotyped Behavior Based on Principal Component Analysis on Autism Diagnostic Interview-Revised},
	journal = {Autism - Open Access},
	volume = {3},
	year = {2013},
	chapter = {107},
	url = {http://www.omicsgroup.org/journals/2165-7890/2165-7890-3-107.pdf},
	author = {Roine, Ulrika and Ripatti, Samuli and Rehnstr{\"o}m, Karola and Timo Roine and Kilpinen, Helena and Surakka, Ida and Wedenoja, Juho and Ylisaukko-oja, Tero and Kempas, Elli and Wessman, Jaana and Moilanen, Irma and Mattila, Marja-Leena and Kielinen, Marko and Jussila, Katja and Suomalainen, Saara and Pulkkinen, Esko and von Wendt, Lennart and Peltonen, Leena}
}
@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}
}
@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}
}
@inproceedings {aleemansjsijberswvdbroekYang2004,
	title = {An Interactive Curvature Based Rigid-body Image Registration Technique: an Application to EFTEM},
	booktitle = {13th European Microscopy Congress},
	year = {2004},
	address = {Antwerp, Belgium},
	author = {Alexander Leemans and Jan Sijbers and Wouter Van den Broek and Z. Yang}
}
@mastersthesis {1286,
	title = {Feature Selection and Classifier Ensembles: A Study on Hyperspectral Remote Sensing Data},
	year = {2003},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Shixin Yu}
}
@article {Yusdbackerpscheund2002,
	title = {Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery},
	journal = {Pattern Recognition Letters},
	volume = {23},
	number = {1},
	year = {2002},
	pages = {183-190},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders}
}
@inproceedings {Yupscheund2001,
	title = {Feature selection for high-dimensional remote sensing data by a maximum entropy principal based optimization},
	booktitle = {Proc. Geoscience and Remote Sensing Symposium},
	year = {2001},
	pages = {3303-3305},
	author = {Shixin Yu and Paul Scheunders}
}
@inproceedings {Yupscheund2001,
	title = {Fuzzy markov chains approach to feature selection for high dimensional remote sensing data},
	booktitle = {Geoscience and Remote Sensing Symposium},
	year = {2001},
	pages = {3306-3308},
	author = {Shixin Yu and Paul Scheunders}
}
@inproceedings {Yupscheundsdbacker2000,
	title = {Genetic Feature selection combined with composite fuzzy nearest neighbor classifers for high-dimensional remote sensing data},
	booktitle = {Proc. IEEE Intern. Conf. on Systems, Man \& Cybernetics, October 8-11, Nashville,TN},
	year = {2000},
	pages = {1912-1916},
	author = {Shixin Yu and Paul Scheunders and Steve De Backer}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic feature selection combined with fuzzy kNN for hyperspectral satellite imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	year = {2000},
	pages = {702-704},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic Feature Selection Combined with Fuzzy K-NN for Hyperspectral Satellite Imagery},
	booktitle = {Intelligent Techniques and Soft Computing in Nuclear Science and Engineering},
	year = {2000},
	pages = {281-288},
	publisher = {World Scientific},
	organization = {World Scientific},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders},
	editor = {d. Ruan, H.A. Abderrahim, P. D{\textquoteright}Hondt, E. Kerre}
}
