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Title: Fission gas bubble identification using MATLAB's image processing toolbox

Abstract

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. This study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding proved to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods. - Highlights: •Automated image processing can aid in the fuel qualification process. •Routines are developed to characterize fissionmore » gas bubbles in irradiated U–Mo fuel. •Frequency domain filtration effectively eliminates FIB curtaining artifacts. •Adaptive thresholding proved to be the most accurate segmentation method. •The techniques established are ready to be applied to large scale data extraction testing.« less

Authors:
 [1]
  1. Colorado School of Mines, Nuclear Sc
Publication Date:
OSTI Identifier:
22689584
Resource Type:
Journal Article
Resource Relation:
Journal Name: Materials Characterization; Journal Volume: 118; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 36 MATERIALS SCIENCE; ALGORITHMS; BUBBLES; CURTAINS; EXTRACTION; FILTRATION; FISSION PRODUCTS; FUEL PLATES; IMAGE PROCESSING; IMAGES; IRRADIATION; M CODES; MICROSTRUCTURE; NOISE; POST-IRRADIATION EXAMINATION; REMOVAL; URANIUM-MOLYBDENUM FUELS; VOIDS

Citation Formats

Collette, R. Fission gas bubble identification using MATLAB's image processing toolbox. United States: N. p., 2016. Web. doi:10.1016/J.MATCHAR.2016.06.010.
Collette, R. Fission gas bubble identification using MATLAB's image processing toolbox. United States. doi:10.1016/J.MATCHAR.2016.06.010.
Collette, R. Mon . "Fission gas bubble identification using MATLAB's image processing toolbox". United States. doi:10.1016/J.MATCHAR.2016.06.010.
@article{osti_22689584,
title = {Fission gas bubble identification using MATLAB's image processing toolbox},
author = {Collette, R.},
abstractNote = {Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. This study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding proved to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods. - Highlights: •Automated image processing can aid in the fuel qualification process. •Routines are developed to characterize fission gas bubbles in irradiated U–Mo fuel. •Frequency domain filtration effectively eliminates FIB curtaining artifacts. •Adaptive thresholding proved to be the most accurate segmentation method. •The techniques established are ready to be applied to large scale data extraction testing.},
doi = {10.1016/J.MATCHAR.2016.06.010},
journal = {Materials Characterization},
number = ,
volume = 118,
place = {United States},
year = {Mon Aug 15 00:00:00 EDT 2016},
month = {Mon Aug 15 00:00:00 EDT 2016}
}