Flexible Unsupervised Binary Change Detection Algorithm Identifies Phase Transitions in Continuous Image Streams

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.