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Pataky TC, Yagi M, Ichihashi N, et al. Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping. PeerJ Comput Sci. 2021;7:e542doi: 10.7717/peerj-cs.542.
Pataky, T. C., Yagi, M., Ichihashi, N., & Cox, P. G. (2021). Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping. PeerJ. Computer science, 7e542. https://doi.org/10.7717/peerj-cs.542
Pataky, Todd C, et al. "Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping." PeerJ. Computer science vol. 7 (2021): e542. doi: https://doi.org/10.7717/peerj-cs.542
Pataky TC, Yagi M, Ichihashi N, Cox PG. Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping. PeerJ Comput Sci. 2021 May 18;7:e542. doi: 10.7717/peerj-cs.542. eCollection 2021. PMID: 34084938; PMCID: PMC8157043.
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PeerJ Comput Sci. 2021 May 18;7:e542. doi: 10.7717/peerj-cs.542. eCollection 2021.

Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping.

PeerJ. Computer science

Todd C Pataky, Masahide Yagi, Noriaki Ichihashi, Philip G Cox

Affiliations

  1. Department of Human Health Sciences, Kyoto University, Kyoto, Japan.
  2. Department of Archaeology, University of York, York, United Kingdom.
  3. Hull York Medical School, University of York, York, United Kingdom.

PMID: 34084938 PMCID: PMC8157043 DOI: 10.7717/peerj-cs.542

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 registration, 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.

©2021 Pataky et al.

Keywords: 2D shape analysis; Classical hypothesis testing; Morphology; Morphometrics; Spatial registration; Statistical analysis

Conflict of interest statement

Philip G. Cox was an Academic Editor for PeerJ.

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