Machine learning for micro-tomography

Dilworth Y. Parkinson; Daniël M. Pelt; Talita Perciano; Daniela Ushizima; Harinarayan Krishnan; Harold S. Barnard; Alastair A. MacDowell; James Sethian

doi:10.1117/12.2274731

26 September 2017 Machine learning for micro-tomography

Dilworth Y. Parkinson, Daniël M. Pelt, Talita Perciano, Daniela Ushizima, Harinarayan Krishnan, Harold S. Barnard, Alastair A. MacDowell, James Sethian

Author Affiliations +

Proceedings Volume 10391, Developments in X-Ray Tomography XI; 103910J (2017) https://doi.org/10.1117/12.2274731
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab–from workstations to supercomputers–and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

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Citation Download Citation

Dilworth Y. Parkinson, Daniël M. Pelt, Talita Perciano, Daniela Ushizima, Harinarayan Krishnan, Harold S. Barnard, Alastair A. MacDowell, and James Sethian "Machine learning for micro-tomography", Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910J (26 September 2017); https://doi.org/10.1117/12.2274731

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