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A new fully automated approach for aligning and comparing shapes.

dc.contributor.author Daubechies, Ingrid
dc.contributor.author Boyer, Douglas
dc.contributor.author Yapuncich, Gabriel
dc.contributor.author Puente, Jesus
dc.contributor.author Gladman, Justin T
dc.contributor.author Glynn, Chris
dc.contributor.author Mukherjee, Sayan
dc.date.accessioned 2019-02-26T17:02:51Z
dc.date.available 2019-02-26T17:02:51Z
dc.date.issued 2015-01
dc.identifier.issn 1932-8486
dc.identifier.issn 1932-8494
dc.identifier.uri https://hdl.handle.net/10161/18083
dc.description.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.
dc.language eng
dc.publisher Wiley
dc.relation.ispartof Anatomical record (Hoboken, N.J. : 2007)
dc.relation.isversionof 10.1002/ar.23084
dc.subject Calcaneus
dc.subject Animals
dc.subject Humans
dc.subject Imaging, Three-Dimensional
dc.subject Anatomy, Comparative
dc.subject Phylogeny
dc.subject Mathematics
dc.subject Algorithms
dc.subject Principal Component Analysis
dc.subject Models, Biological
dc.subject Automation
dc.subject Software
dc.title A new fully automated approach for aligning and comparing shapes.
dc.type Journal article
dc.date.updated 2019-02-26T17:02:50Z
pubs.begin-page 249
pubs.end-page 276
pubs.issue 1
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group Duke
pubs.organisational-group Mathematics
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Evolutionary Anthropology
pubs.organisational-group Staff
pubs.publication-status Published
pubs.volume 298


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