Novel algorithms for image classification and visualisation of multispectral datasets

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The recent technological improvements in microscopy now allows the collection of extensive information ofsamples at the nanoscale. However, in many cases, the amount of data collected surpasses the currentvisualization capacity making their interpretation and analysis impractical. While two and three physicaldimensions visualization is quite intuitive, the interpretation of datasets with a higher number of dimensionssuch as with chemical tomography, is less intuitive. Nevertheless, the information provided by extradimensions is essential when attempting to uncover similarities or correlations essential for materialsdevelopment (e.g. distinguish phases in a material, identify and map impurities).The research group of Pr. Nadi Braidy have been successful in developing a classification algorithm to simplifythe interpretation of multi-spectral datasets. However, the efficiency of the algorithm and the code is notadapted to large datasets (i.e. several giga/terabytes). The goal of this project is to develop and implementscientific and software improvements to the existing algorithm to improve its computational efficiency. Therevamped algorithm will be implemented into Object Research System (ORS) Inc. visualization software,Dragonfly.In the framework of this Engage, the collaboration between Pr. Braidy and ORS will provide ORS with a toolto handle multi-modal datasets in its software while providing support in improving its efficiency.