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Landmark-free, Parametric Hypothesis Tests Regarding Two-dimensional Contour Shapes Using Coherent Point Drift Registration and Statistical Parametric Mapping

Pataky, TC; Yagi, M; Ichihashi, N; Cox, PG; (2021) Landmark-free, Parametric Hypothesis Tests Regarding Two-dimensional Contour Shapes Using Coherent Point Drift Registration and Statistical Parametric Mapping. PeerJ Computer Science , 7 , Article e542. 10.7717/PEERJ-CS.542. Green open access

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Abstract

This paper proposes a computational framework for automated, landmark-free hypothesis testing of 2D contour shapes (i.e., shape outlines), and implements one realization of that framework. The proposed framework consists of point set regis-tration, point correspondence determination, and parametric full-shape hypothesis testing. The results are calculated quickly (<2 s), yield morphologically rich detail in an easy-to-understand visualization, and are complimented by parametrically (or nonparametrically) calculated probability values. These probability values represent the likelihood that, in the absence of a true shape effect, smooth, random Gaussian shape changes would yield an effect as large as the observed one. This proposed framework nevertheless possesses a number of limitations, including sensitivity to algorithm parameters. As a number of algorithms and algorithm parameters could be substituted at each stage in the proposed data processing chain, sensitivity analysis would be necessary for robust statistical conclusions. In this paper, the proposed technique is applied to nine public datasets using a two-sample design, and an ANCOVA design is then applied to a synthetic dataset to demonstrate how the proposed method generalizes to the family of classical hypothesis tests. Extension to the analysis of 3D shapes is discussed.

Type: Article
Title: Landmark-free, Parametric Hypothesis Tests Regarding Two-dimensional Contour Shapes Using Coherent Point Drift Registration and Statistical Parametric Mapping
Open access status: An open access version is available from UCL Discovery
DOI: 10.7717/PEERJ-CS.542
Publisher version: https://doi.org/10.7717/peerj-cs.542
Language: English
Additional information: Copyright © 2021 Pataky et al. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
Keywords: Morphology, Morphometrics, 2D shape analysis, Statistical analysis, Classical hypothesis testing, Spatial registration
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Cell and Developmental Biology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
URI: https://discovery.ucl.ac.uk/id/eprint/10156717
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