Statistical and Topological Quantification of Shape Features in Biomedical Imaging

This project develops new methods for medical image analysis by combining techniques from statistical and topological data analysis. The overall objective is to robustly detect morphological features in biomedical images obtained by label-free 'finger-printing' vibrational spectroscopy methods and classify the images according to their disease status. Such advanced spectroscopy and novel imaging methods are able to extract the chemical composition of biological systems, including living cells, to an unprecedented level of detail, and thus can be used for accurate medical diagnosis and early detection of diseases. The imaging techniques give multicomponent spectral information bearing chemical and structural information that can be complex to disentangle in a robust non-subjective manner. However, at the moment, the diagnosis depends solely on the clinician's expertise, while machine learning data-driven classifiers are limited by the typically small number of training samples. This project will develop novel statistical models which use topological summaries to create powerful methods for image classification without the need of vast training sets.