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Statistical Analysis of Functions on Surfaces, with an application to Medical Imaging

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STSW02 - Statistics of geometric features and new data types

Co-author: John Aston (University of Cambridge)

In Functional Data Analysis, data are commonly assumed to be smooth functions on a fixed interval of the real line. In this work, we introduce a comprehensive framework for the analysis of functional data, whose domain is a two-dimensional manifold and the domain itself is subject to variability from sample to sample. We formulate a statistical model for such data, that we call Functions on Surfaces, which enables a joint representation of the geometric and functional aspects, and propose an associated estimation framework. We apply the proposed framework to the analysis of neuroimaging data of cortical thickness, acquired from the brains of different subjects, and thus lying on domains with different geometries.

This talk is part of the Isaac Newton Institute Seminar Series series.

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