# Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
###### Abstract  
Grain boundaries (GBs), which are among the mechanical properties of a material, are a microstructural aspect that contributes to the overall behavior of metal. A deep understanding of the behavior of the GBs' deformation, dislocation, and fracture will encourage the rapid development of new materials and lead to the better operation and maintenance of materials during their designed lifetimes. In this study, an integrated image processing toolset is proposed to provide an expeditious approach to extracting GBs, tracking their location, and identifying their internal deformation. This toolset consists of three integrated algorithms: image stitching, grain matching, and boundary extraction. The algorithms are designed to simultaneously integrate high and low spatial resolution images for gathering high-precision boundary coordinates and effectively reconstructing a view of the entire material surface for the tracing of the grain location. This significantly reduces the time needed to acquire the dataset owing to the ability of the low spatial resolution lens to capture wider areas as the base image. The high spatial resolution lens compensates for any weakness of the base image by capturing views of specific sections, thereby increasing the observation flexibility. One application successfully described in this paper is tracking the direction of the metal grain deformation in global coordinates by stacking a specific grain before and after the deformation. This allows observers to calculate the direction of the grain deformation by comparing the overlapping areas after the material experiences a load. Ultimately, this toolset is expected to lead to further applications in terms of observing fascinating phenomena in materials science and engineering. grain boundary; image stitching; grain matching; boundary extraction; gaussian adaptive image binarization +
Footnote †: journal: Medical Imaging  
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can substantially facilitate microstructural analysis and modeling, thus shortening the time to the development of new materials and leading to the better operation and maintenance of such materials during their service life [16; 17; 18]. The microstructure of a metal consists of aggregates of grains that have diverse crystallographic lattice orientations with respect to their nearest neighboring grains [19] and unique bimodal histogram distributions. The past several years have witnessed rapid progress in investigating GB-mediated deformation using a variety of in situ and ex situ experimental techniques [20; 21; 22; 23]. One rapid approach to extracting the metal GBs is accomplished via image processing and optical measurements. The edge-detection method is used to extract the GBs by detecting meaningful discontinuities in the pixel intensity values [24]. Several edge-detection methods have been proposed in the literature, but they are generally grouped into Gradient and Laplacian approaches [25; 26; 27]. These edge-detection approaches provide various levels of sophistication and computational efficiency by reducing the amount of data required and filtering out the useless features while preserving information regarding the significant structural properties of the GBs [28]. Implementing image binarization before the edge-detection algorithm provides a robust tracking ability with low storage requirements and straightforward processing compared to tracking discontinuous values on gray-level images [29; 30]. Many researchers agree that the combination of image pre-processing approaches and image binarization enhances the clarity of an image, thus providing flexible methods for extracting the GBs [26]. Figure 1 shows a comparison of the edge detection on gray-level and binary images using gradient and Laplacian filters. Image binarization is an image transformation algorithm used to transform a grayscale image into a bi-level intensity image by converting any existing gradation [26].