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Proceedings Paper

Machine learning for micro-tomography
Author(s): Dilworth Y. Parkinson; Daniël M. Pelt; Talita Perciano; Daniela Ushizima; Harinarayan Krishnan; Harold S. Barnard; Alastair A. MacDowell; James Sethian
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Paper 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.

Paper Details

Date Published: 26 September 2017
PDF: 8 pages
Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910J (26 September 2017); doi: 10.1117/12.2274731
Show Author Affiliations
Dilworth Y. Parkinson, Lawrence Berkeley National Lab. (United States)
Daniël M. Pelt, Lawrence Berkeley National Lab. (United States)
Talita Perciano, Lawrence Berkeley National Lab. (United States)
Daniela Ushizima, Lawrence Berkeley National Lab. (United States)
Univ. of California, Berkeley (United States)
Harinarayan Krishnan, Lawrence Berkeley National Lab. (United States)
Harold S. Barnard, Lawrence Berkeley National Lab. (United States)
Alastair A. MacDowell, Lawrence Berkeley National Lab. (United States)
James Sethian, Lawrence Berkeley National Lab. (United States)
Univ. of California, Berkeley (United States)


Published in SPIE Proceedings Vol. 10391:
Developments in X-Ray Tomography XI
Bert Müller; Ge Wang, Editor(s)

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