Flexible unsupervised binary change detection algorithm identifies phase transitions in continuous image streams
1 University of Michigan, Ann Arbor, MI; 2 Brookhaven National Laboratory, Upton, NY
Abstract: Sequences of projection images collected during in-situ tomography experiments can capture the formation of patterns in crystallization and yield their three-dimensional growth morphologies. These image streams generate enormous and high dimensional datasets that span the full extent of a phase transition. Detecting from the continuous image stream the characteristic times and temperatures at which the phase transition initiates is a challenge because the phase change is often swift and subtle. Here we show a flexible unsupervised binary classification algorithm to identify a change point during data intensive experiments. The algorithm makes a prediction based on statistical metrics and has a quantifiable error bound. Applied to two in-situ X-ray tomography experimental datasets collected at a synchrotron light source, the developed method can detect the moment at which the solid phase emerges from the parent liquid phase upon crystallization and without performing computationally expensive volume reconstructions. Our approach is verified using a simulated X-ray phantom and its performance evaluated with respect to solidification parameters. The method presented here can be broadly applied to other big data problems where time series can be classified without the need for additional training data.
Keywords: Solidification, nucleation, machine learning, sinogram, x-ray tomography
Install the conda enviroment dependencies by running:
$ conda env create -f environment.yamlExplore the Example.ipynb jupyter notebook to get started. The sinogram data used in the example is available on Dropbox. Full datasets are available as well. The first case study regarding quasicrystal formation is available through the Materials Data Facility repository. The second case study regarding primary silicon formation in chemically-modified alloy is available through the University of Michigan Deep Blue repository.
The Matlab codes to simulate the phantoms are also available.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.