A graph-theoretic approach for characterization of precipitates from atom probe tomography data

Samudrala, Saritha; Wodo, Olga; Suram, Santosh K.; Broderick, Scott; Rajan, Krishna; Ganapathysubramanian, Baskar

doi:10.1016/j.commatsci.2013.04.038

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…Once segmentation was done, the connected components labeling method (Suzuki et al, 2003; Gonzales and Woods, 2008; Wodo et al, 2012; Samudrala et al, 2013; Pace et al, 2014) was used on the processed image to remove spurious outliers and noise from the image (e.g., plant debris on soil). This was accomplished by identifying clusters of pixels that connected to one another, followed by labeling them, and identifying the largest connected component (i.e., plants in a plot).…”

Section: Methodsmentioning

confidence: 99%

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

et al. 2017

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Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use.

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Section: Methodsmentioning

confidence: 99%

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

et al. 2017

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“…In this "reduced" dimensional space, we can then seek to find patterns and associations of data that permit us to unravel the complexity and truly "mine" the rich information embedded in atom probe images and spectra. The process of reducing the dimensionality of data mathematically is one that has to be done carefully, to avoid losing or distorting true correlations between characteristics in the original data set [73,[131][132][133][134]. There are numerous techniques to accomplish this and in the following discussion we shall provide a couple of brief examples of the value of using such methods.…”

Section: Data Dimensionalitymentioning

confidence: 97%

Mining information from atom probe data

et al. 2015

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Whilst atom probe tomography (APT) is a powerful technique with the capacity to gather information containing hundreds of millions of atoms from a single specimen, the ability to effectively use this information creates significant challenges. The main technological bottleneck lies in handling the extremely large amounts of data on spatial-chemical correlations, as well as developing new quantitative computational foundations for image reconstruction that target critical and transformative problems in materials science. The power to explore materials at the atomic scale with the extraordinary level of sensitivity of detection offered by atom probe tomography has not been not fully harnessed due to the challenges of dealing with missing, sparse and often noisy data. Hence there is a profound need to couple the analytical tools to deal with the data challenges with the experimental issues associated with this instrument. In this paper we provide a summary of some key issues associated with the challenges, and solutions to extract or "mine" fundamental materials science information from that data.

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“…However, we must acknowledge that these algorithms may not www.advancedsciencenews.com www.aem-journal.com be optimal for all purposes. There are many other algorithms that have been developed and considered for APT data analysis, some similar to maximum separation and DBSCAN, [120][121][122] some employing mixture models, [123] and others dependent upon computational or networking geometry. [124][125][126] This should not be regarded as an exhaustive list and there are many more algorithms besides which have not yet been applied to APT data analysis.…”

Section: Clusterfinding and Algorithmsmentioning

confidence: 99%

Clustering in Age‐Hardenable Aluminum Alloys

et al. 2018

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This review gives an overview of the effects of clusters in various aluminum alloys. Characterization methods are discussed in general and results for the important AlMgSi alloys are presented in detail. Indirect characterization methods, such as hardness, tensile testing, electrical resistivity, differential scanning calorimetry, and positron annihilation spectroscopy are discussed, as well as atom probe tomography for the direct measurement of clusters. A particular focus is set on atom probe tomography, where possible artifacts influencing the cluster measurements as well as different cluster finding methods are summed up. A comprehensive summary of investigated alloys and cluster algorithm parameters is given. Moreover, the findings in AlMgSi alloys regarding clusters and changes upon different heat treatments are discussed, starting from early to the latest works. Drawn conclusions are discussed and compared to give a r esum e.

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A graph-theoretic approach for characterization of precipitates from atom probe tomography data

Cited by 17 publications

References 31 publications

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Mining information from atom probe data

Clustering in Age‐Hardenable Aluminum Alloys

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