Joost Batenburg
Professor Computer science
- Name
- Prof.dr. K.J. Batenburg
- Telephone
- +31 71 527 6985
- k.j.batenburg@liacs.leidenuniv.nl
Joost Batenburg is a professor at LIACS and his chair is Imaging and Visualization. He is also affiliated with the CWI and is program director of the interdisciplinary research programme Society, Artificial Intelligence and Life Sciences (SAILS). He published more than 80 journal articles and more than 60 conference papers in the field of tomographic image processing and reconstruction.
More information about Joost Batenburg
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Vici grants for 7 Leiden researchers
Joost Batenburg is a professor at LIACS and his chair is Imaging and Visualization. He is also affiliated with the CWI and is program director of the interdisciplinary research programme Society, Artificial Intelligence and Life Sciences (SAILS). He published more than 80 journal articles and more than 60 conference papers in the field of tomographic image processing and reconstruction.
From 2013 till 2017 he chaired the EU COST Action EXTREMA on advanced X-ray tomography. He pioneered the field of discrete tomography, developing the first large-scale reconstruction methods.
His current research focuses on creating a real-time tomography pipeline, funded by an NWO Vici grant. He is responsible for the FleX-Ray lab, where a custom-designed CT system is linked to advanced data processing and reconstruction algorithms.
Professor Computer science
- Science
- Leiden Inst of Advanced Computer Science
- Sero D., Garachon I., Hermens E. & Batenburg K.J. (2023), Artist profiling using micro-CT scanning of a Rijksmuseum terracotta sculpture, Science Advances 9(38): eadg6073.
- Skorikov A., Batenburg K.J. & Bals S. (2023), Analysis of 3D elemental distribution in nanomaterials: towards higher throughput and dose efficiency, Journal of Microscopy 289(3): 157-163.
- Kiss M.B., Bossema F.G., Laar P.J.C. van, Meijer S., Lucka F., Leeuwen T. van & Batenburg K.J. (2023), Beam filtration for object-tailored X-ray CT of multi-material cultural heritage objects, Heritage Science 11(1): 130.
- Andriiashen V., Liere R. van, Leeuwen T. van & Batenburg K.J. (2023), CT-based data generation for foreign object detection on a single X-ray projection, Scientific Reports 13(1): 1881.
- Kavak S., Kadu A.A., Claes N., Sánchez-Iglesia A., Liz-Marzán L.M., Batenburg K.J. & Bals S. (2023), Quantitative 3D investigation of nanoparticle assemblies by volumetric segmentation of electron tomography data sets, The Journal of Physical Chemistry Part C 127(20): 9725-9734.
- Schoonhoven R.A., Werkhoven B.J.C. van & Batenburg K.J. (2023), Benchmarking optimization algorithms for auto-tuning GPU kernels, IEEE Transactions on Evolutionary Computation 27(3): 550-564.
- Kiss Maximilian B. Coban Sophia B. Batenburg K. Joost van Leeuwen Tristan Lucka Felix (2023), 2DeteCT-A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning, Scientific Data 10(1): 576.
- Kim J., Pelt D.M., Kagias M., Stampanoni M., Batenburg K.J. & Marone F. (2022), Tomographic reconstruction of the small-angle x-ray scattering tensor with filtered back projection, Physical Review Applied 18(1): 014043.
- Minnema J., Ernst A., Eijnatten M. van, Pauwels R., Forouzanfar T., Batenburg K.J. & Wolff J. (2022), A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery, Dentomaxillofacial Radiology 51(7): 20210437.
- Dorscheid J., Bossema F.G., Duin P. van, Coban S.B., Liere R. van, Batenburg K.J. & Stefano G.P. di (2022), Looking under the skin: multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum, Heritage Science 10(1): 161.
- Ganguly P.S., Lucka F., Kohr H., Franken E., Hupkes H.J. & Batenburg K.J. (2022), SparseAlign: a grid-free algorithm for automatic marker localization and deformation estimation in cryo-electron tomography, IEEE Transactions on Computational Imaging 8: 651-665.
- Pelt D.M., Hendriksen A.A. & Batenburg K.J. (2022), Foam-like phantoms for comparing tomography algorithms, Journal of Synchrotron Radiation 29: 254-265.
- Zeegers M.T., Kadu A., Leeuwen T. van & Batenburg K.J. (2022), ADJUST: a dictionary-based joint reconstruction and unmixing method for spectral tomography, Inverse Problems 38(12): 125002.
- Zeegers M.T., Leeuwen T. van, Pelt D.M., Coban S.B., Liere R. van & Batenburg K.J. (2022), A tomographic workflow to enable deep learning for X-ray based foreign object detection, Expert Systems with Applications 206: 117768.
- Hendriksen A.A., Schut D., Palenstijn W.J., Viganó N., Kim J., Pelt D.M., Leeuwen T. van & Batenburg K.J. (2021), Tomosipo: Fast, flexible, and convenient 3D tomography for complex scanning geometries in Python, Optics Express 29(24): 40494-40513.
- Hendriksen A.A., Bührer M., Leone L., Marlini M., Vigano N., Pelt D.M., Marone F., Michiel M. di & Batenburg K.J. (2021), Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data, Scientific Reports 11(1): 11895.
- Ganguly P.S., Pelt D.M., Gürsoy D., Carlo F. de & Batenburg K.J. (2021), Improving reproducibility in synchrotron tomography using implementation-adapted filters, Journal of Synchrotron Radiation 28(5): 1583-1597.
- Lagerwerf M.J., Pelt D.M., Palenstijn W.J. & Batenburg K.J. (2020), A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks, Journal of Imaging 6(12): 135.
- Hendriksen A.A., Pelt D.M. & Batenburg K.J. (2020), Noise2Inverse: self-supervised deep convolutional denoising for tomography, IEEE Transactions on Computational Imaging 6: 1320-1335.
- Vanrompay H., Buurlage J.W., Pelt D.M., Kumar Vi., Zhuo X., Liz-Marzán L.M., Bals S. & Batenburg K.J. (2020), Real‐time reconstruction of arbitrary slices for quantitative and in situ 3D characterization of nanoparticles, Particle & Particle Systems Characterization 37(7): 2000073.
- Zeegers M.T., Pelt D.M., Leeuwen T. van, Liere R. van & Batenburg K.J. (2020), Task-driven learned hyperspectral data reduction using end-to-end supervised deep learning, Journal of Imaging 6(12): 132.
- Batenburg K.J., Helwerda L.S., Kosters W.A. & Meij T. van der (2017), Mobile Radio Tomography: Agent-Based Imaging. Bosse T. & Bredeweg B. (Eds.), BNAIC 2016: Artificial Intelligence. 28th Benelux Conference on Artificial Intelligence (BNAIC 2016) 10 November 2016 - 11 November 2016. Communications in Computer and Information Science no. 765. Cham: Springer. 63-77.
- Palenstijn W.J., Bédorf J., Sijbers J. & Batenburg K.J. (2016), A distributed ASTRA toolbox, Advanced Structural and Chemical Imaging 2(1): 19.
- Batenburg K.J., Helwerda L.S., Kosters W.A. & Meij T. van der (2016), Agents for mobile radio tomography, Proceedings BNAIC 2016. 28th Benelux Conference on Artificial Intelligence (BNAIC 2016) 10 November 2016 - 11 November 2016 17-24.
- Bleichrodt F., Leeuwen T. van, Palenstijn W.J., Aarle W. van, Sijbers J. & Batenburg K.J. (2016), Easy implementation of advanced tomography algorithms using the ASTRA toolbox with Spot operators, Numerical Algorithms 71(3): 673-697.
- Bladt E., Pelt D.M., Bals S. & Batenburg K.J. (2015), Electron tomography based on highly limited data using a neural network reconstruction technique, Ultramicroscopy 158: 81-88.
- Plantagie L. & Batenburg K.J. (2015), Algebraic filter approach for fast approximation of nonlinear tomographic reconstruction methods, Journal of Electronic Imaging 24: 013026.
- Zhuge X., Palenstijn W.J. & Batenburg K.J. (2015), TVR-DART: a more robust algorithm for discrete tomography from limited projection data with automated gray value estimation, IEEE Transactions on Image Processing 25(1): 455-468.
- Fortes W., Hajdu L. & Tijdeman R. (2013), Bounds on the quality of reconstructed images in binary tomography, Discrete Applied Mathematics 161(15): 2236-2251.
- Pelt D.M. (2013), Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks, IEEE Transactions on Image Processing 22(12): 5238-5251.
- Palenstijn W.J., Balazs P. & Sijbers J. (2013), Dynamic angle selection in binary tomography, Computer vision and image understanding : CVIU 117(4): 306-318.
- Fortes W. & Tijdeman R. (2013), Approximate Discrete Reconstruction Algorithm, Fundamenta Informaticae 125(3-4): 239-259.
- Member Scientific Advisory Council
- deelname Wetenschappelijke Adviesraad (SAB)
- Onderzoeker Computational Imaging (0.2fte)
- Deelname Wetenschappelijke Adviesraad