lmfree2d: Landmark-free hypothesis tests regarding 2D contour shapes

Overview

This repository contains data, Python and R code for conducting parametric or nonparametric, landmark-free hypothesis testing regarding 2D contour shapes. Data in this repository are redistributed from the The 2D Shape Structure Dataset under the terms of its MIT license.

Landmark-free hypothesis testing results look like those in the figure below. These results represent mass-multivariate analysis of registered 2D contour data, using Statistical Parametric Mapping and Statistical non-Parametric Mapping.

This figure depicts mean shapes for each of two groups (A and B), an omnibus p value (representing the probability that smooth, bivariate continuum data would randomly produce a mean difference as large as the largest observed difference), and highlighted contour points (hot-colored circles) representing locations where shape effects were largest.

Getting started

The best place to start is the notebooks (in the Notebooks folder), which summarize four different approaches to hypothesis testing for 2D contour shapes:

1. landmarks_uv:     univariate analysis of landmarks (i.e., Procrustes ANOVA)
2. landmarks_massmv:     mass-multivariate analysis of landmarks
3. contours_uv:     landmark-free, univariate analysis of registered contours (i.e., contour-level Procrustes ANOVA)
4. contours_massmv:     landmark-free, mass-multivariate analysis of registered contours using Statistical Parametric Mapping

Note that the results in the figure above correspond to the final method: contours_massmv.

Please refer to the notebooks and the papers below for more details.

Dependencies:

Python:

• geomdl
• networkx
• pandas
• pycpd
• sklearn
• spm1d

R:

• geomorph

Support

For support, please submit an issue here.

Please do not email the authors directly. Email requests for support will be routed to this repository's issues site on GitHub.

Please cite:

Pataky TC, Yagi M, Ichihashi N, Cox PG (2021). Automated, landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping. PeerJ Comp Sci 7:e542. https://doi.org/10.7717/peerj-cs.542

Carlier, A., Leonard, K., Hahmann, S., Morin, G., and Collins, M. (2016). The 2D shape structure dataset: a user annotated open access database. Computers & Graphics 58: 23–30.