Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Functional Data Analysis for Imaging Mean Function Estimation: Computing Times and Parameter Selection

Version 1 : Received: 6 April 2022 / Approved: 8 April 2022 / Online: 8 April 2022 (03:21:06 CEST)

How to cite: Arias López, J.; Cadarso Suárez, C.; Aguiar Fernández, P. Functional Data Analysis for Imaging Mean Function Estimation: Computing Times and Parameter Selection. Preprints 2022, 2022040068 (doi: 10.20944/preprints202204.0068.v1). Arias López, J.; Cadarso Suárez, C.; Aguiar Fernández, P. Functional Data Analysis for Imaging Mean Function Estimation: Computing Times and Parameter Selection. Preprints 2022, 2022040068 (doi: 10.20944/preprints202204.0068.v1).

Abstract

Functional Data Analysis (FDA) is a relatively new field of statistics dealing with data expressed in the form of functions. FDA methodologies can be easily extended to the study of imaging data, an application proposed in Wang et al. (2020), where the authors settle the mathematical groundwork and properties of the proposed estimators. This methodology allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. This is especially relevant for the field of medical imaging, as one of the most extended research setups consists on the comparison between two groups of images, a pathological set against a control set. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups (Arias-López et al., 2021). In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases and then measure computing times for the construction of Delaunay triangulations, and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that previous researcher has been too conservative in its parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging.

Keywords

Functional Data Analysis; Image Processing; Brain Imaging; Neuroimaging; Computational Neuroscience; Data Science

Subject

MATHEMATICS & COMPUTER SCIENCE, Computational Mathematics

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