@article {2423, title = {Framework for optimal design of a multi-source rectangular X-ray cargo scanning system}, journal = {Radiation Physics and Chemistry}, volume = {239}, year = {2026}, pages = {113332}, doi = {https://doi.org/10.1016/j.radphyschem.2025.113332 }, author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and Jan De Beenhouwer and Jan Sijbers} } @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 {2393, title = {Single X-ray Projection Material Decomposition using a Mesh Projector}, booktitle = {14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium (iCT 2025)}, year = {2025}, author = {Fleur Linsen and Domenico Iuso and Jan Sijbers} } @article {2323, title = {CAD-ASTRA: A versatile and efficient mesh projector for X-ray tomography with the ASTRA-toolbox}, journal = {Optics Express}, volume = {32}, year = {2024}, pages = {3425-3439}, doi = {https://doi.org/10.1364/OE.498194}, author = {Pavel Paramonov and Nicholas Francken and Jens Renders and Domenico Iuso and Tim Elberfeld and Jan De Beenhouwer and Jan Sijbers} } @inproceedings {2374, title = {Deep image prior for sparse-view reconstruction in static, rectangular multi-source x-ray CT systems for cargo scanning}, booktitle = {SPIE Developments in X-Ray Tomography XV}, volume = {13152}, year = {2024}, doi = {https://doi.org/10.1117/12.3027668}, author = {Caroline Bossuyt and Arnold Jan den Dekker and Domenico Iuso and T. Le Hoang and Julie Escoda and Marius Costin and Jan Sijbers and Jan De Beenhouwer} } @mastersthesis {2413, title = {Ensuring Flawlessness in Additive Manufacturing: Advances in X-ray Inspection Techniques for Efficient Defect Detection}, volume = {PhD in Sciences}, year = {2024}, type = {PhD thesis}, author = {Domenico Iuso} } @inproceedings {2350, title = {High-resolution tiled X-ray cone-beam CT using the ASTRA toolbox}, booktitle = {13th Conference on Industrial Computed Tomography (iCT) 2024}, year = {2024}, month = {03/2024}, address = {School of Engineering, Wels Campus, Austria}, abstract = {Dealing with large and/or elongated objects presents a challenge in micro computed tomography ({\textmu}CT). In conventional CT, the necessity for the X-ray beam to fully cover the scanned object which has to remain well within the detector boundaries is essential to avoid region-of-interest artifacts in the reconstructed images. Consequently, the magnification of the object and thereby the spatial resolution are limited by the physical dimensions and degrees of freedom of the imaging system. A possible way to increase the magnification and thus the resolution of a {\textmu}CT image is to perform a tiled scan of the object. In this paper, we propose an iterative reconstruction approach that yields a high-resolution image from such a tiled CT scan. The approach utilizes the ASTRA toolbox, leveraging its key features: the vector-based definition of projection geometry and the linear tomography operators. Our simulations demonstrate that the proposed approach achieves higher resolution through increased magnification.}, keywords = {cone beam, iterative reconstruction, micro computed tomography, X-ray tomography}, isbn = {1435-4934}, doi = {10.58286/29258 }, author = {Pavel Paramonov and Joaquim G. Sanctorum and Domenico Iuso and Jan Sijbers and Jan De Beenhouwer} } @article {2358, title = {PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection}, journal = {Precision Engineering}, volume = {90}, year = {2024}, pages = {108-121}, doi = {https://doi.org/10.1016/j.precisioneng.2024.08.006}, author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers} } @article {2336, title = {Practical multi-mesh registration for few-view poly-chromatic X-ray inspection}, journal = {Journal of Non-destructive Testing}, volume = {43}, year = {2024}, chapter = {63}, doi = {10.1007/s10921-024-01071-y}, author = {Domenico Iuso and Pavel Paramonov and Jan De Beenhouwer and Jan Sijbers} } @article {2349, title = {Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural models}, journal = {Applied Intelligence}, volume = {54}, year = {2024}, pages = { 13160{\textendash}13177}, doi = {https://doi.org/10.1007/s10489-024-05647-z}, author = {Domenico Iuso and Soumick Chatterjee and Sven Cornelissen and Dries Verhees and Jan De Beenhouwer and Jan Sijbers} } @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} } @inproceedings {2227, title = {Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression}, booktitle = {11th International Conference on Robotics, Vision, Signal Processing and Power Applications}, volume = {829}, year = {2022}, publisher = {Lecture Notes in Electrical Engineering, Springer}, organization = {Lecture Notes in Electrical Engineering, Springer}, address = {Singapore}, doi = {10.1007/978-981-16-8129-5_66}, author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Paul Scheunders} } @inproceedings {2209, title = {Evaluation of a Morphable Anthropomorphic Articulated Total Body Model}, booktitle = {Design Tools and Methods in Industrial Engineering II - Proceedings of the 2nd International Conference on Design Tools and Methods in Industrial Engineering, ADM 2021}, year = {2022}, pages = {761{\textendash}772}, publisher = {Springer}, organization = {Springer}, abstract = {In this work a new approach for the creation of Articulated Total Body (ATB) models for person-specific multi-body simulations is presented, with the main aim of overcoming limitations related to classical multi-ellipsoids ATB models, based on regression equations having only the weight and the height of the subject as input. The new methodology is based on a Statistical Shape Model (SSM), morphable according to up to 24 input parameters: the SSM was obtained from Principal Component Analysis (PCA), applied on a wide database of 3D human scans (CAESAR). The so obtained geometry can be segmented automatically to generate body segments with the respective inertial properties (mass, principal moments of inertia, and centres of mass location). The routine has been tested on a random set of 20 male subjects and the classical multi-ellipsoids models were compared to these in terms of inertial properties and 3D external geometry: the highest differences were registered at the abdomen and the thighs for what concerns the mass (60\%), principal moments (75\%) and centres of mass (50 mm) properties; the trunk, the shoulder and the calves are the most critical areas for the external geometry (average distance between the anthropomorphic and ellipsoids models equal to 50 mm). A contribution has been made to build person-specific multibody models. This is a valuable method since approximations made by multi-ellipsoidal models have resulted to be relevant at specific body areas, and personalised models can be a support to design and to forensic analyses.}, keywords = {3D parametric human model, Articulated total body, Forensic biomechanics, Multibody analysis, principal component analysis (PCA)}, isbn = {978-3-030-91233-8}, doi = {10.1007/978-3-030-91234-5_77}, author = {Giulia Pascoletti and Toon Huysmans and Paolo Conti and Zanetti, Elisabetta M.}, editor = {Caterina Rizzi and Francesca Campana and Michele Bici and Francesco Gherardini and Tommaso Ingrassia and Paolo Cicconi} } @inproceedings {2243, title = {Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing}, booktitle = {SPIE Optical Engineering: Developments in X-Ray Tomography XIV }, volume = {12242}, year = {2022}, pages = {122421K}, doi = {https://doi.org/10.1117/12.2633318}, author = {Domenico Iuso and Soumick Chatterjee and Rob Heylen and Sven Cornelissen and Jan De Beenhouwer and Jan Sijbers} } @article {2409, title = {Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress}, journal = {Computers and Electronics in Agriculture}, volume = {195}, year = {2022}, pages = {106806}, abstract = {Conventional methods to access plant physiological traits are based on destructive measurements by means of biochemical extraction or leaf clipping, thereby limiting the throughput capability. With advances in hyperspectral imaging sensor, fast, non-invasive and non-destructive measurements of a plant{\textquoteright}s physiological status became feasible. In this work, a non-destructive method for the characterization of a plant{\textquoteright}s status from hyperspectral images is presented. A supervised data-driven method based on Machine Learning Regression (MLR) algorithms was developed to generate prediction models of four targeted physiological traits: water potential, effective quantum yield of photosystem II, transpiration rate and stomatal conductance. Standard Normal Variate (SNV) transformed reflectance spectra were used as the input variables for building the regression model. Three MLR algorithms: Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Partial Least Squares Regression (PLSR) were explored as candidate methods for building the prediction model of the targeted physiological traits. Validation results show that the non-linear prediction models, developed based on the GPR algorithm produced the best estimation accuracy on all plant traits. The best prediction models were applied to a small-scale phenotyping experiment to study drought stress responses in maize plants. Results show that all estimated traits revealed a significant difference between plants under drought stress and normal growth dynamics as early as after 3 days of drought induction.}, keywords = {Drought stress, Hyperspectral imaging, Machine learning regression, Non-destructive measurements, Physiological traits, Plant phenotyping}, issn = {0168-1699}, doi = {https://doi.org/10.1016/j.compag.2022.106806}, url = {https://www.sciencedirect.com/science/article/pii/S0168169922001235}, author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Koirala Bikram and Paul Scheunders} } @article {2221, title = {Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress}, journal = {Computers and Electronics in Agriculture}, volume = {195}, year = {2022}, doi = {10.1016/j.compag.2022.106806}, author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Bikram Koirala and Paul Scheunders} } @article {2248, title = {Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography.}, journal = {Int J Comput Assist Radiol Surg}, year = {2022}, month = {2022 Mar 12}, abstract = {To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system{\textquoteright}s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use.

METHODS: A novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively.

RESULTS: The method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are [Formula: see text]~mm and [Formula: see text]~mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from [Formula: see text] to [Formula: see text]~mm.

CONCLUSION: A semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters.}, issn = {1861-6429}, doi = {10.1007/s11548-022-02580-9}, author = {Chabi, Negar and Domenico Iuso and Beuing, Oliver and Preim, Bernhard and Saalfeld, Sylvia} } @inproceedings {2172, title = {CAD-based scatter compensation for polychromatic reconstruction of additive manufactured parts}, booktitle = {IEEE ICIP}, year = {2021}, pages = {2948-2952}, doi = {10.1109/ICIP42928.2021.9506536}, author = {Domenico Iuso and Ehsan Nazemi and Nathana{\"e}l Six and Bjorn De Samber and Jan De Beenhouwer and Jan Sijbers} } @article {2179, title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge}, journal = {NeuroImage}, volume = {240}, year = {2021}, doi = {https://doi.org/10.1016/j.neuroimage.2021.118367}, author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fernandez and Evren Ozarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fenxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas and Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G Schilling} } @article {2163, title = {Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction}, journal = {Applied Sciences}, volume = {11}, year = {2021}, chapter = {3145}, doi = {https://doi.org/10.3390/app11073145}, author = {Ehsan Nazemi and Nathana{\"e}l Six and Domenico Iuso and Bjorn De Samber and Jan Sijbers and Jan De Beenhouwer} } @conference {2094, title = {Strategies for quantifying the 3D atomic structure and the dynamics of nanomaterials using model-based STEM}, year = {2020}, author = {Sandra Van Aert and Annick De Backer and De wael, A and Jarmo Fatermans and Arslan Irmak, E and Friedrich, T and Ivan Lobato and Jones, L and Arnold Jan den Dekker and Peter D Nellist and Sara Bals} } @article {2007, title = {Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform}, journal = {Computers and Electronics in Agriculture}, volume = {162}, year = {2019}, pages = {749-758}, doi = {https://doi.org/10.1016/j.compag.2019.05.018}, author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders} } @article {1901, title = {Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing }, volume = {138}, year = {2018}, pages = {121-138}, author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders} } @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} } @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} }