Integrative Morphometrics Using 3D Imaging

Authors

  • Jonas Ivanov Assistant Professor, School of Data Science, Nordic Technical University, Stockholm, Sweden Author
  • Eva Klein Senior Lecturer, Institute of Intelligent Systems, Baltic AI Research University, Tallinn, Estonia Author
  • Noah Petrov Assistant Professor, Department of Computer Science, Western Europe Data Science University, Madrid, Spain Author

Keywords:

geometric morphometrics, micro-CT, photogrammetry, 3D imaging, landmark analysis, museum specimens, shape analysis, zoological methodology

Abstract

Three-dimensional imaging technologies -- micro-computed tomography (micro-CT), photogrammetry, structured light scanning, and laser scanning -- have transformed morphometric analysis in zoology over the past decade by enabling non-destructive capture of complete three-dimensional shape information from biological specimens at resolutions from sub-millimetre (micro-CT) to whole-body surface (photogrammetry). Integration of these technologies with geometric morphometric analytical frameworks -- generalised Procrustes analysis, principal components analysis of shape coordinates, thin-plate spline deformation grids -- enables quantification of morphological variation at a level of completeness and precision previously accessible only for 2D outlines or manually measured linear distances. This methodological review and benchmark study evaluates the relative performance of four 3D imaging modalities across 18 zoological applications from micro-invertebrate morphology to large mammal skeletal analysis, testing precision, accuracy, processing time, and cost-effectiveness on 247 specimens from 84 species across 18 higher taxa. Micro-CT achieved the highest accuracy for internal and complex external morphology (mean landmark RMS error 0.024 mm) but at the highest cost and longest processing time. Photogrammetry provided comparable external surface accuracy (0.047 mm) at 12.4% of micro-CT cost and 84.7% faster processing. A decision framework for modality selection based on specimen size, target morphological complexity, available budget, and required precision is provided, with open-source software recommendations for each application context.

Author Biographies

  • Jonas Ivanov, Assistant Professor, School of Data Science, Nordic Technical University, Stockholm, Sweden

    Assistant Professor, School of Data Science, Nordic Technical University, Stockholm, Sweden

  • Eva Klein, Senior Lecturer, Institute of Intelligent Systems, Baltic AI Research University, Tallinn, Estonia

    Senior Lecturer, Institute of Intelligent Systems, Baltic AI Research University, Tallinn, Estonia

  • Noah Petrov, Assistant Professor, Department of Computer Science, Western Europe Data Science University, Madrid, Spain

    Assistant Professor, Department of Computer Science, Western Europe Data Science University, Madrid, Spain

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Published

2024-03-15

How to Cite

Integrative Morphometrics Using 3D Imaging. (2024). Zoological Archives: An International Journal, 4(1), 41-50. https://stanfordgroup.org/index.php/ZAIJ/article/view/348