Carl Zeiss X-ray Microscopy, Inc.

Carl Zeiss Microscopy Gmbh

Matthew Andrew

William B Thompson

Christoph Hilmar Graf Vom Hagen

William Dean Thompson

Lars Omlor

Edward Hill

Joaquin Correa

Richard Moralee

Evan Drake

Andriy Andreyev

Hrishikesh Bale

Benjamin Tordoff

William Thompson

Benjamin Tordorff

A multi scale material segmentation method is provided that creates markers to identify unique particles, for small and large particles independently, and then separately processes those markers.

3D particle analysis and separation using dual seeding

A method of enhancing a resolution of an EDS image of a sample includes generating an EDS image of the sample, generating a non-EDS image of the sample generating, using a machine learning algorithm, an enhanced resolution EDS image of the sample based on the generated feature map and based on the first EDS image, where a resolution of the enhanced resolution EDS image is higher than a resolution of the first EDS image.

Grain-based minerology segmentation system and method

Multivariant feature extraction is used for training volumes or 2D images, (real or synthetic) coupled to process (effective) values probably obtained from direct simulation. These features are coupled with machine learning/regression algorithms to make a predictive model for the effective property. This model can then be used on a real geometry of a sample for effective parameter prediction.

Process parameter prediction using multivariant structural regression

Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Accessible neural network image processing workflow

A x-ray micro tomography system provides the ability to proscriptively determine regularization parameters for iterative reconstruction of a sample, from projection data of the sample. This allows a less experienced operator to determine the regularization parameters with adequate precision.

System and method for the proscriptive determination of parameters for iterative reconstruction

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

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Pleasanton, CA, United States of America

Matthew Andrew