Description
Leader of the group Computational Imaging: Joost Batenburg.
Our research group is developing the next generation of 3D imaging – enabling scientists to look further into objects of all kinds. Based on mathematics, algorithms and numerical solution techniques, our approach is interdisciplinary, combining aspects of mathematics, computer science and physics. We pride ourselves on the versatility of our solutions, and our algorithms can be applied to a wide range of imaging in science, medicine and industry. In Computational Imaging, it’s our goal to constantly push the boundaries of research. By combining advanced image acquisition, parameter estimation, and discrete tomography algorithms for example, we are able to develop workflows for 3D electron microscopy at atomic resolution.
News
ERCIM News 108 on Computational Imaging co-coordinated by Joost Batenburg
In January 2017, ERCIM News No. 108 was published: http://ercim-news.ercim.eu/en108. It features a special theme on Computational Imaging, coordinated by guest editors Joost Batenburg (CWI) and Tamas Sziranyi (MTA SZTAKI).
Faster computation of high-quality 3D images
PhD student Daniël Pelt of CWI has developed new algorithms for computer tomography (CT) for fast computation of high-quality three-dimensional images. This increases the practical use of this technology in areas such as material sciences and biomedical research.
Vici grant for Joost Batenburg
Researcher Joost Batenburg of CWI has been awarded a Vici grant from the Netherlands Organisation for Scientific Research (NWO) for his proposal 'Real-Time 3D Tomography'. With the 1.5 million euro grant, Batenburg can develop his own research line in the next five years.
CWI reduces perturbations in tomography
Researcher Folkert Bleichrodt of CWI has developed new methods to improve the applicability of tomography. These methods reduce unwanted perturbations. This allows for applying tomography on a very small scale or based on a small number of measurements.
Members
- Vladyslav Andriiashen
- Joost Batenburg
- Francien Bossema
- Sophia Coban
- Poulami Ganguly
- Adriaan Graas
- Allard Hendriksen
- Ajinkya Kadu
- Rien Lagerwerf
- Tristan van Leeuwen
- Robert van Liere
- Felix Lucka
- Willem Jan Palenstijn
- Daniël Pelt
- Georgios Pilikos
- Richard Schoonhoven
- Dirk Schut
- Dzemila Sero
- Mathé Zeegers
Associated Members
Publications
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Buurlage, J, Marone, F, Pelt, D.M, Palenstijn, W.J, Stampanoni, M, Batenburg, K.J, & Schlepütz, C.M. (2019). Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT. Nature Scientific Reports, 9(1). doi:10.1038/s41598-019-54647-4
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Der Sarkissian, H.H, Lucka, F, van Eijnatten, M.A.J.M, Colacicco, G, Coban, S.B, & Batenburg, K.J. (2019). A cone-beam X-ray computed tomography data collection designed for machine learning. Scientific Data, 6(1). doi:10.1038/s41597-019-0235-y
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Colman, K.L, de Boer, H.H, Dobbe, J.G.G, Liberton, N.P.T.J, Stull, K.E, van Eijnatten, M.A.J.M, … van der Merwe, A.E. (2019). Virtual forensic anthropology: The accuracy of osteometric analysis of 3D bone models derived from clinical computed tomography (CT) scans. Forensic Science International, 304. doi:10.1016/j.forsciint.2019.109963
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Sero, D, Zaidi, A, Li, J, White, J.D, González Zarzar, T.B.G, Marazita, M.L, … Claes, P. (2019). Facial recognition from DNA using face-to-DNA classifiers. Nature Communications, 10(1). doi:10.1038/s41467-019-10617-y
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Hendriksen, A.A, Pelt, D.M, Palenstijn, W.J, Coban, S.B, & Batenburg, K.J. (2019). On-the-fly machine learning for improving image resolution in tomography. Applied Sciences, 9(12). doi:10.3390/app9122445
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Bossema, F.G, Burger, J.P, Bratton, L, Challenger, A, Adams, R.C, Sumner, P, … Smeets, I. (2019). Expert quotes and exaggeration in health news: A retrospective quantitative content analysis. Wellcome Open Research. doi:10.12688/wellcomeopenres.15147.2
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Viganò, N.R, Martínez Gil, P.M, Herzog, C, de la Rochefoucauld, O, Liere, R.V. (Robert Van), & Batenburg, K.J. (2019). Advanced light-field refocusing through tomographic modeling of the photographed scene. Optics Express, 27(6), 7834–7856. doi:10.1364/OE.27.007834
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Pelt, D.M, Batenburg, K.J, & Sethian, J.A. (2018). Improving tomographic reconstruction from limited data using Mixed-Scale Dense convolutional neural networks. Journal of Imaging, 4(11), 128–128. doi:10.3390/jimaging4110128
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Tick, J, Pulkkinen, A, Lucka, F, Ellwood, R, Cox, B.T, Kaipio, J.P. (Jari), … Tarvainen, T. (2018). Three dimensional photoacoustic tomography in Bayesian framework. The Journal of the Acoustical Society of America, 144(4), 2061–2071. doi:10.1121/1.5057109
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Der Sarkissian, H.H, Viganò, N.R, & Batenburg, K.J. (2018). A data consistent variational segmentation approach suitable for real-time tomography. Fundamenta Informaticae, 163(1), 1–20. doi:10.3233/FI-2018-1729
Software
Current projects with external funding
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Machine learning for large 3D tomographic images ()
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Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()
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Non-destructive 3D spectral imaging: applications in the poultry industry ()
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PPS contract Planmeca ()
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Real-Time 3D Tomography ()
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Cambridge G102101: HPV mediated cervical lesions
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CT for Art: from Images to Patterns (IMPACT4Art)
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MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
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Translation-Driven Development of Deep Learning for Simultaneous Tomographic Image Reconstruction and Segmentation (None)
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Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
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Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA)
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Enabling X-ray CT based Industry 4.0 process chains by training Next Generation research experts (xCTing)
Related partners
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Fraunhofer Gesellschaft
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Naturalis
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Rijksmuseum Amsterdam
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Universiteit Wageningen
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University of Cambridge
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GREEFA
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Katholieke Universiteit Leuven
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Rheinisch-Westfaelische Technische Hochschule Aachen
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Universiteit Utrecht