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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. Finally, we conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images.

Authors:
ORCiD logo [1];  [2];  [3];  [3];  [4];  [4]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
  3. NASA Ames Research Center, Moffett Field, CA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Advanced Light Source (ALS)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1597715
Alternate Identifier(s):
OSTI ID: 1527203
Grant/Contract Number:  
[AC02-05CH11231; AC02‐05CH11231]
Resource Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
[ Journal Volume: 12; Journal Issue: 4]; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 3D image processing; 3D segmentation; 3D woven carbon fiber; composites; machine learning; microCT; neural networks

Citation Formats

MacNeil, J. Michael L., Ushizima, Daniela M., Panerai, Francesco, Mansour, Nagi N., Barnard, Harold S., and Parkinson, Dilworth Y. Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means. United States: N. p., 2019. Web. doi:10.1002/sam.11429.
MacNeil, J. Michael L., Ushizima, Daniela M., Panerai, Francesco, Mansour, Nagi N., Barnard, Harold S., & Parkinson, Dilworth Y. Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means. United States. doi:10.1002/sam.11429.
MacNeil, J. Michael L., Ushizima, Daniela M., Panerai, Francesco, Mansour, Nagi N., Barnard, Harold S., and Parkinson, Dilworth Y. Thu . "Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means". United States. doi:10.1002/sam.11429.
@article{osti_1597715,
title = {Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means},
author = {MacNeil, J. Michael L. and Ushizima, Daniela M. and Panerai, Francesco and Mansour, Nagi N. and Barnard, Harold S. and Parkinson, Dilworth Y.},
abstractNote = {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. Finally, we conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images.},
doi = {10.1002/sam.11429},
journal = {Statistical Analysis and Data Mining},
number = [4],
volume = [12],
place = {United States},
year = {2019},
month = {6}
}

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