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