A new fully automated approach for aligning and comparing shapes.
Abstract
Three-dimensional geometric morphometric (3DGM) methods for placing landmarks on digitized
bones have become increasingly sophisticated in the last 20 years, including greater
degrees of automation. One aspect shared by all 3DGM methods is that the researcher
must designate initial landmarks. Thus, researcher interpretations of homology and
correspondence are required for and influence representations of shape. We present
an algorithm allowing fully automatic placement of correspondence points on samples
of 3D digital models representing bones of different individuals/species, which can
then be input into standard 3DGM software and analyzed with dimension reduction techniques.
We test this algorithm against several samples, primarily a dataset of 106 primate
calcanei represented by 1,024 correspondence points per bone. Results of our automated
analysis of these samples are compared to a published study using a traditional 3DGM
approach with 27 landmarks on each bone. Data were analyzed with morphologika(2.5)
and PAST. Our analyses returned strong correlations between principal component scores,
similar variance partitioning among components, and similarities between the shape
spaces generated by the automatic and traditional methods. While cluster analyses
of both automatically generated and traditional datasets produced broadly similar
patterns, there were also differences. Overall these results suggest to us that automatic
quantifications can lead to shape spaces that are as meaningful as those based on
observer landmarks, thereby presenting potential to save time in data collection,
increase completeness of morphological quantification, eliminate observer error, and
allow comparisons of shape diversity between different types of bones. We provide
an R package for implementing this analysis.
Type
Journal articleSubject
CalcaneusAnimals
Humans
Imaging, Three-Dimensional
Anatomy, Comparative
Phylogeny
Mathematics
Algorithms
Principal Component Analysis
Models, Biological
Automation
Software
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https://hdl.handle.net/10161/18083Published Version (Please cite this version)
10.1002/ar.23084Publication Info
Boyer, Doug M; Puente, Jesus; Gladman, Justin T; Glynn, Chris; Mukherjee, Sayan; Yapuncich,
Gabriel S; & Daubechies, Ingrid (2015). A new fully automated approach for aligning and comparing shapes. Anatomical record (Hoboken, N.J. : 2007), 298(1). pp. 249-276. 10.1002/ar.23084. Retrieved from https://hdl.handle.net/10161/18083.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Douglas Martin Boyer
Associate Professor of Evolutionary Anthropology
Ingrid Daubechies
James B. Duke Distinguished Professor of Mathematics and Electrical and Computer Engineering
Gabriel Yapuncich
Assistant Professor of the Practice of Medical Education
I hail from the great mountain states of Montana (the state of my birth) and Wyoming
(the state of my childhood). I have a bachelor's degree in English literature from
the University of Wisconsin and a bachelor's degree in the evolutionary biology from
Columbia University. I completed my PhD in evolutionary anthropology at Duke University
in March 2017, working with Dr. Doug M. Boyer. I have taught gross and microanatomy
to Duke University School of Medicine students since 2018. In 2021, I
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