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 postirradiation 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.

Determination of the degree of grain refinement in irradiated U-Mo fuels

“…As was the case when characterizing fission gas bubbles, it is noted that the mechanical preparation relative to preparation with a focused ion beam relieves the necessity for filters to remove curtaining artifacts [5, 6, 7, 8]. The major difference between the characterization of fission gas bubbles and the determination of the degree of grain refinement is the optimal image magnification to be acquired from the microscope leading to ideal results.…”

“…Certain analyses of the data and images generated from post-irradiation examination can be quite complex. Thus, development of easily-repeatable baseline methods of analysis that can be directly compared by various researchers to calibrate results for relative comparison is of interest [5, 6, 7, 8]. Development of such baseline approaches not only allows for easier comparison of results from various efforts, but also allows for easier communication of more complex analytical methods that can be presented as deviations from baseline methods.…”

Microstructural study of apple slices during hot air‐explosion puffing drying

“…Following this, the processed images were combined with MATLAB software to write programs to calculate tissue cell cross‐sectional area, cross‐sectional perimeter, and so forth. By utilizing the gathered data, other microstructural parameters were calculated (Bardsley et al, 2021; Collette et al, 2016; Ravikumar & Arulmozhi, 2022). The image processing process is shown in Figure 4.…”

Matlab Framework for Image Processing and Feature Extraction Flexible Algorithm Design

“…Automatic characterization of materials and material structures is an essential tool for the speed and accuracy of their quality assessment. Algorithms such as those built for analyzing the cupping profiles of laminated wood products [8], the identification of fission gas bubbles [9], or for the extraction of information from SEM micrographs of nanotube structures [10] fall into this category. Developing, testing, tweaking, extending, and improving such algorithms is an iterative process that could be greatly sped up and eased by using a dedicated framework for flexible algorithm design.…”