IDENTIFICATION OF FISSION GAS VOIDS
Ryan Collette
Introduction
‣ The Reduced Enrichment of Research and Test Reactor (RERTR)
program aims to convert fuels from high to low enrichment in order
to meet non-proliferation goals
‣ High atom density requirements in test reactors has forced the
development of new fuel types
‣ Extensive research into uranium-molybdenum plate-type fuels
‣ Microstructural characterization of these fuels can provide
information as to their behavior under various irradiation conditions
Fission Gas Bubbles
‣ Release of gaseous fission products (Xenon, Krypton) into
fuel matrix during irradiation can lead to fuel swelling and
possible delamination of the cladding
‣ Porosity determinations (bubble count, distribution, volume
fraction) can aid in the evaluation of fuel performance
‣ Current methods involve hand counts and visual inspection
of images
‣ An automated methodology could allow for more precise
correlations
‣ Similar studies are limited, but cells provide a valid parallel
Cell Segmentation Methods
‣ Thresholding
‣ Pre-processing
– Global thresholding
– Notch filtering
– Local adaptive thresholding
– Noise removal
‣ Edge detection
– Linking procedure
‣ Feature matching
‣ Region growing
– Watershed
– Feature smoothing
– Grayscale morphology
‣ Post-processing
– Binary morphology
– Data extraction
Image Considerations
‣ Images provided by Idaho
National Laboratory
‣ Samples milled with a
focused ion beam (FIB)
and imaged with a
scanning electron
microscope (SEM)
‣ FIB milling creates a
curtaining effect in the
image
Sample FIB-SEM images
Preprocessing – Removing the curtaining
‣ Frequency domain filtering
– High frequencies correlate to edges in image
– Low frequencies represent areas with more constant gray levels
‣ Notch filter using FFT (Fast Fourier Transform)
‣ Basic algorithm
– Convert spatial image to frequency domain
– Shift spectrum for visualization
– Identify frequencies corresponding to uniform edges
– Attenuate those frequencies (zero out with a mask)
– Return to spatial domain
Notch filtration example
Noise reduction
‣ Histogram equalization and contrast
adjustment reveals significant
peppering between void events
‣ Techniques tested:
– Global averaging filter
‧ Median/Gaussian blur out small
voids and leave halos
– Wiener filter
‧ Effective, but ultimately not
enough information about the
noise
– Anisotropic diffusion
‧ Deals with aliasing well, but
orientation capability irrelevant
– Bilateral filtering
‧ Simple edge preserving
smoothing but suits the needs of
this project
Bilateral Algorithm
•
Two Gaussians operate on localized
pixel neighborhood
• Domain Filter (Spatial)
• Range Filter (Intensity)
•
Pixels with very different intensity
values are weighted less even though
they may have close proximity to
central pixel
•
Behaves similarly to standard domain
filter in smooth regions
•
At edges, similarity function will ignore
pixels on opposite side of step edge
and average similar intensity pixels in
the vicinity
Thresholding
‣ Otsu Global
– Uneven illumination in
some images makes this
unrealistic
‣ Local adaptive
– Threshold varies on per
pixel basis based on image
characteristics
‣ Sauvola method
– Threshold computed using
the mean and standard
deviation of pixel
intensities in window
around target pixel
Edge detection
‣ Sobel, Prewitt, Roberts
– Identify only edges we want
– Rarely captures the full contour of the void
– Hysteresis edge linking algorithm improves connectivity, but less prominent voids are
lost
‣ In conclusion, edge detection methods are not precise enough to warrant consideration
over thresholding methods
Post-processing
‣ Binary morphological operations
– Openings and closing to combine or divide connected components
– Filling of holes within objects
– Clearing of objects touching borders
‣ Regionprops
– Object count - Size distribution - Total void volume fraction
Porosity: 3.8%
Hand count: 294 events
Matlab: 286 events
Sample data correlations
File name
Count Hand Count
Deviation (%) Mean Area (µm^2) Area Fraction (%)
MZ‐50C_XS_Site 1
41
46
10.8
0.020
1.03
MZ‐50C_XS_Site 2
65
58
12.1
0.028
1.57
MZ‐50C_XS_Site 3
93
87
6.9
0.026
2.16
MZ‐50C_XS_Site 4
107
104
2.9
0.024
2.33
MZ‐50C_XS_Site 5
76
68
11.7
0.027
2.40
MZ‐50C_XS_Site 6
279
244
14.3
0.016
2.82
MZ‐50C_XS_Site 7
329
313
5.1
0.014
2.56
MZ‐50C_XS_Site 8
284
267
6.4
0.014
2.40
Issues and possible improvements
‣ Identifying voids as singular events remains problematic due to the sharp
gradients created by solid fission products
‣ Method susceptible to false positive identifications based on background texture
– Possible feature detection system
‣ Lower magnification images identified to a higher degree of confidence
‣ Verification necessary to assure accuracy of segmentation
– Hand counts subjective. What is a gas void and what isn’t?
– Synthetic images?
‣ Sample preparation and imaging conditions need to be consistent to make the
process repeatable