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Changes in the visual characteristics of uranium oxide surfaces and morphology following storage under different conditions of temperature and relative humidity may provide insight into the history of an unknown sample. Sub-samples of three α-U3O8 materials—one that was phase-pure and two that were phase-impure—were stored under controlled conditions for two years. Scanning electron microscopy was used to image the oxides before and after storage, and a morphology lexicon was used to characterize the images. Temporal changes in morphology were observed in some sub-samples, and changes were greatest following exposure to high relative humidity.

An understanding of the relationships between morphologic signatures of uranium oxide materials and precursor materials used to prepare them would be valuable for determining process history of intercepted materials. Here, three different high purity uranium oxide materials were prepared from a single source of uranyl peroxide hydrate (UO2(O2)·xH2O) to determine variability of texture between the precipitate precursor and calcination products. Scanning electron microscopy images of the materials were characterized using a lexicon of descriptors. Analyses revealed that morphologic textures of calcination products were indeed equivalent to that of the UO2(O2)·xH2O precipitate, revealing a morphologic signature for forensic analyses of process history.

We have developed a lexicon to consistently and objectively describe morphological features observed in scanning electron microscopy (SEM) images. Here we provide the lexicon flowsheet, define the terminology, and detail step-by-step characterization of SEM images collected from a set of actinide oxides. We conclude that this lexicon can be used to characterize texture and surface features, particle structure and size, and grain boundaries in an image of a material. The lexicon should be applicable to characterization of images collected from other techniques for measuring morphology, as well. LA-UR-15-26746

In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.