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Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets

Published online by Cambridge University Press:  18 June 2019

Alexander F. Brust ,
Eric J. Payton ,
Toren J. Hobbs  and
Stephen R. Niezgoda
Alexander F. Brust
Affiliation:
Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
Eric J. Payton
Affiliation:
Air Force Research Laboratory, Materials and Manufacturing Directorate, Dayton, OH 45433, USA
Toren J. Hobbs
Affiliation:
Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
Stephen R. Niezgoda*
Affiliation:
Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43210, USA
*
*Author for correspondence: Stephen R. Niezgoda, E-mail: [email protected]
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Abstract

Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.

Keywords

atomic clusteringBayesian statisticsgraph cuttingsegmentation
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 25 , Issue 4 , August 2019 , pp. 924 - 941
Copyright
Copyright © Microscopy Society of America 2019 

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