Clustering and segmentation of chemical imaging datasets

Through this project we seek to identify and develop methods to automatically segment complex chemical imaging datasets, e.g. XRD-CT, Raman mapping and other hyperspectral imaging techniques. The goal is to identify the minimum number of unique chemical environments in a dataset, without needing prior knowledge or input as to the expected identity or number of components present.

The output from this segmentation will then be used to inform subsequent data quantification and analysis steps, and also to see if it is possible to identify correlations between each of these components. A successful outcome will be of benefit to a broad range of industry and chemical services companies, as many analytical methods suffer from similar segmentation challenges.