Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means

MacNeil, J. Michael L.; Ushizima, Daniela M.; Panerai, Francesco; Mansour, Nagi N.; Barnard, Harold S.; Parkinson, Dilworth Y.

doi:10.1002/sam.11429

Title: Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means

Journal Article · 20 June 2019 · Statistical Analysis and Data Mining

DOI: https://doi.org/10.1002/sam.11429 · OSTI ID:1597715

^[1]; Ushizima, Daniela M. ^[2]; Panerai, Francesco ^[3]; Mansour, Nagi N. ^[3]; Barnard, Harold S. ^[4]; Parkinson, Dilworth Y. ^[4]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
NASA Ames Research Center, Moffett Field, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Advanced Light Source (ALS)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1597715

Journal Information:: Statistical Analysis and Data Mining, Journal Name: Statistical Analysis and Data Mining Journal Issue: 4 Vol. 12; ISSN 1932-1864

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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