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
Jeroen Hazewinkel wins prize for best presentation
At the annual meeting of the J.M. Burgers Centrum (the Research School for Fluid Dynamics) Jeroen Hazewinkel of the Centrum Wiskunde & Informatica (CWI) has been awarded the first prize for his presentation about tomographical (3D) reconstructions of internal water waves.
Better image quality for virtual reality with computer science
Less image delays, less 'image echoes' and more fluid movements in virtual reality (VR): this is all possible with the research of Ferdi Smit of the Centrum Wiskunde & Informatica (CWI), the Dutch national research center for mathematics and computer science in Amsterdam, the Netherlands.
Faster biomedical image processing with CWI research
Alexander Broersen defended his PhD thesis on biomedical visualization on 3 March.
Mathematics to reveal atomic structure wins first Philips Mathematics Prize
Can physicists obtain a 3D image of the positions of all individual atoms inside tiny crystals?
Members
Associated Members
Publications
-
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
-
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
-
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
-
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 (Switzerland), 9(12). doi:10.3390/app9122445
-
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
-
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
-
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
-
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
-
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
-
Hauptmann, A, Arridge, S, Lucka, F, Muthurangu, V, & Steeden, J.A. (2018). Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning–proof of concept in congenital heart disease. Magnetic Resonance in Medicine. doi:10.1002/mrm.27480
Software
Current projects with external funding
-
Machine learning for large 3D tomographic images ()
-
Non-destructive 3D spectral imaging: applications in the poultry industry ()
-
Real-Time 3D Tomography ()
-
Mathematics and Algorithms for 3D Imaging of Dynamic Processes
-
PPS contract Planmeca
-
CT for Art: from Images to Patterns (IMPACT4Art)
-
Combining cycloidal computed tomography with machine learning: a mechanism to disrupt the costly relationship between spatial resolution and radiation dose (ML-CYCLO-CT)
-
MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
-
Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
-
Volumetric medical x-ray imaging at extremely low dose (VOXEL)
Related partners
-
Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
-
CNR Pisa
-
COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
-
IMAGINE OPTIC
-
Naturalis
-
Planmeca
-
Rijksmuseum Amsterdam
-
Universidad Politécnica de Madrid
-
University College London