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IDEAL: Images Across Domains, Experiments, Algorithms and Learning

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Abstract

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

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Notes

  1. https://github.com/CameraIA/F3D.

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Acknowledgements

This work was supported by the Director, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences, of the US Department of Energy. Both the Early Career Research project and the Center for Applied Mathematics for Energy Related Applications (CAMERA) are under Contract No. DE-AC02- 05CH11231. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02- 05CH11231. We would like to thank Przemyslaw Oberbek, Materials Design Division, Faculty of Materials Science and Engineering, Warsaw University of Technology, Warsaw, Poland for preparing the SEM images. Also, the authors thank P. Nico and A. Wills for sharing samples of microCT of geological materials, and STEM of PMO, respectively. Additional thanks to B. Loring for supporting visualization schemes, and M. Alegro for participating on the development of multimodal registration methods.

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Authors and Affiliations

  1. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela M. Ushizima, E. Wes Bethel, Harinarayan Krishnan, Maciej Haranczyk, Talita Perciano & Chao Yang

  2. Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Hrishikesh A. Bale & Robert O. Ritchie

  3. Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Peter Ercius & Brett A. Helms

  4. Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA

    Lea T. Grinberg

  5. Advanced Light Source Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Alastair A. Macdowell & Dilworth Y. Parkinson

  6. Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdańsk, Poland

    Katarzyna Odziomek

  7. University of California Berkeley, Berkeley, CA, USA

    Daniela M. Ushizima & Robert O. Ritchie

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  1. Daniela M. Ushizima
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Correspondence to Daniela M. Ushizima.

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Ushizima, D.M., Bale, H.A., Bethel, E.W. et al. IDEAL: Images Across Domains, Experiments, Algorithms and Learning. JOM 68, 2963–2972 (2016). https://doi.org/10.1007/s11837-016-2098-4

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