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Shape-changing chains for morphometric analysis of 2D and 3D, open or closed outlines
Bingjue Li1, Shengmin Zhou2, Andrew Peter Murray3
1School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro/Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing, 211189, Jiangsu, China. libj@seu.edu.cn.
Scientific Reports
|November 3, 2021
Summary
This study introduces a novel shape-changing chain method for morphometrics, simplifying complex shape analysis. This technique offers a more interpretable and efficient approach for biological and medical shape studies.
Area of Science:
- Morphometrics
- Geometric Morphometrics
- Shape Analysis
Background:
- Traditional morphometrics often involves numerous variables, complicating analysis and interpretation, especially for complex shapes or large datasets.
- Existing methods can be difficult to apply to diverse 2D or 3D profiles and may yield results lacking clear physical meaning.
Purpose of the Study:
- To present a new methodology for synthesizing shape-changing chains for 2D/3D curve fitting.
- To utilize shape-changing chain parameters in stepwise discriminant analysis (DA).
Main Methods:
- Developed a shape-changing chain model with rigid, scalable, and extendible segments.
- Applied the chain fitting to 2D profiles (fossil hominin mandibles, leaf outlines) and 3D curves (infant skull sutures).
- Used stepwise discriminant analysis (DA) on the derived chain parameters.
Main Results:
- The shape-changing chain method successfully analyzed diverse 2D and 3D profiles, including open/closed and smooth/serrated curves.
- Segmentation is flexible, automatic, and incorporates biological/geometric features.
- The method yields a modest number of variables for statistical analysis with physically meaningful parameters.
Conclusions:
- The shape-changing chain offers advantages over common morphometric techniques, providing a flexible, efficient, and interpretable approach to shape analysis.
- This method is broadly applicable across various biological, medical, and paleoanthropological datasets.
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