@article{091e08849cfa4e14ad7ec1a17d98936f,
title = "Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means",
abstract = "This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images.",
keywords = "3D image processing, 3D segmentation, 3D woven carbon fiber, composites, machine learning, microCT, neural networks",
author = "MacNeil, {J. Michael L.} and Ushizima, {Daniela M.} and Francesco Panerai and Mansour, {Nagi N.} and Barnard, {Harold S.} and Parkinson, {Dilworth Y.}",
note = "Funding Information: This work was supported by the Office of Science, of the U.S. Department of Energy (DOE) under Contract No. DE-AC02-05CH11231, and the Moore-Sloan Foundation. This work is partially supported by the DOE Advanced Scientific Computing Research (ASCR), Early Career Research Project, Image across Domains, Experiments, Algorithms and Learning (IDEAL), as well as the Center for Applied Mathematics for Energy Research Applications (CAMERA), led by James Sethian, with support from Basic Energy Sciences (BES) and ASCR within DOE. This research used resources of the Advanced Light Source, which is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DOE or the University of California. The work of F.P and N.N.M. is supported by the NASA Entry Systems Modeling (ESM) project. We would also like thank the anonymous reviewers who significantly improved the quality of the manuscript. Funding Information: information NASA Entry Systems andModelling Project; Office of Science, DE-AC02-05CH11231; NASA Entry Systems Modeling (ESM) project; DOE Office of Science User Facility, DE-AC02-05CH11231; Center for Applied Mathematics for Energy Research Applications (CAMERA); Image across Domains, Experiments, Algorithms and Learning (IDEAL); Early Career Research Project; DOE Advanced Scientific Computing Research; Moore-Sloan Foundation; Office of Science, of the U.S. Department of Energy, DE-AC02-05CH11231This work was supported by the Office of Science, of the U.S. Department of Energy (DOE) under Contract No. DE-AC02-05CH11231, and the Moore-Sloan Foundation. This work is partially supported by the DOE Advanced Scientific Computing Research (ASCR), Early Career Research Project, Image across Domains, Experiments, Algorithms and Learning (IDEAL), as well as the Center for Applied Mathematics for Energy Research Applications (CAMERA), led by James Sethian, with support from Basic Energy Sciences (BES) and ASCR within DOE. This research used resources of the Advanced Light Source, which is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DOE or the University of California. The work of F.P and N.N.M. is supported by the NASA Entry Systems Modeling (ESM) project. We would also like thank the anonymous reviewers who significantly improved the quality of the manuscript. Funding Information: NASA Entry Systems and Modelling Project; Office of Science, DE-AC02-05CH11231; NASA Entry Systems Modeling (ESM) project; DOE Office of Science User Facility, DE-AC02-05CH11231; Center for Applied Mathematics for Energy Research Applications (CAMERA); Image across Domains, Experiments, Algorithms and Learning (IDEAL); Early Career Research Project; DOE Advanced Scientific Computing Research; Moore-Sloan Foundation; Office of Science, of the U.S. Department of Energy, DE-AC02-05CH11231 ABBREVIATIONS: LBP, local binary pattern; LTV, local total variation; microCT, micro-tomography; MLP, multilayer perceptron; NLM, nonlocal means; NN, neural network; RF, random forest. Publisher Copyright: {\textcopyright} 2019 Wiley Periodicals, Inc.",
year = "2019",
month = aug,
doi = "10.1002/sam.11429",
language = "English (US)",
volume = "12",
pages = "338--353",
journal = "Statistical Analysis and Data Mining",
issn = "1932-1872",
publisher = "John Wiley & Sons, Ltd.",
number = "4",
}