Fission gas bubble identification using MATLAB's image processing toolbox

Collette, R.; King, J.; Keiser, Jr., D.; Miller, B.; Madden, J.; Schulthess, J.

doi:10.1016/j.matchar.2016.06.010

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

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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. In addition, 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.

Authors:

Collette, R. ^[1];

^[1]; Keiser, Jr., D. ^[2]; Miller, B. ^[2]; Madden, J. ^[2]; Schulthess, J. ^[2]

Colorado School of Mines, Golden, CO (United States)
Idaho National Lab. (INL), Idaho Falls, ID (United States)

Publication Date:: Wed Jun 08 00:00:00 EDT 2016

Research Org.:: Idaho National Lab. (INL), Idaho Falls, ID (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1294583

Report Number(s):: INL/JOU-15-35421
Journal ID: ISSN 1044-5803; PII: S1044580316301760

Grant/Contract Number:: 00140302

Resource Type:: Accepted Manuscript

Journal Name:: Materials Characterization

Additional Journal Information:: Journal Volume: 118; Journal Issue: C; Journal ID: ISSN 1044-5803

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS; 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; nuclear fuel; MATLAB; automated image analysis; fission bubbles; frequency domain filtration; segmentation

Citation Formats


                    Collette, R., King, J., Keiser, Jr., D., Miller, B., Madden, J., and Schulthess, J. Fission gas bubble identification using MATLAB's image processing toolbox.  United States: N. p., 2016. 
Web.  doi:10.1016/j.matchar.2016.06.010.

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                    Collette, R., King, J., Keiser, Jr., D., Miller, B., Madden, J., & Schulthess, J. Fission gas bubble identification using MATLAB's image processing toolbox.  United States.  https://doi.org/10.1016/j.matchar.2016.06.010

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                    Collette, R., King, J., Keiser, Jr., D., Miller, B., Madden, J., and Schulthess, J. Wed .  
"Fission gas bubble identification using MATLAB's image processing toolbox".  United States.  https://doi.org/10.1016/j.matchar.2016.06.010.  https://www.osti.gov/servlets/purl/1294583.

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@article{osti_1294583,

  title        = {Fission gas bubble identification using MATLAB's image processing toolbox},

  author       = {Collette, R. and King, J. and Keiser, Jr., D. and Miller, B. and Madden, J. and Schulthess, J.},

  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. In addition, 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.},

  doi          = {10.1016/j.matchar.2016.06.010},

  journal      = {Materials Characterization},

  number       = C,

  volume       = 118,

  place        = {United States},

  year         = {Wed Jun 08 00:00:00 EDT 2016},

  month        = {Wed Jun 08 00:00:00 EDT 2016}

}

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