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Practical Morphometrics Analysis (3D Model)
Rating: 4.2 out of 5(23 ratings)
2,216 students

Practical Morphometrics Analysis (3D Model)

A step-by-step approach to morphometrics based on three-dimensional images
Created byOlalekan Agbolade
Last updated 2/2020
English

What you'll learn

  • Introduction to morphometrics covering definitions, traditional morphometrics and geometric morphometrics
  • Landmarks and acqusition tools covering landmark types (anatomical or biological, mathematical, pseudo-landmarks), landmark homology, landmark acquisition tools and how to use, and error assessment with Procrustes ANOVA
  • Landmarks Visualization covering General Procrustes Analysis (GPA), visualization tools, scatter plots of landmark coordinates, Principal Component Analysis (PCA)
  • Statistical methods and Analysis covering ANOVA, MANOVA, ANOSIM/PERMANOVA, regression & allometry, discriminant analysis and canonical variates analysis, clustering, EDMA

Course content

4 sections • 22 lectures • 3h 47m total length

  • Background concept to morphometrics7:43
  • Landmark and Acquisition in 3D14:01
  • Landmark Visualization7:11
  • Statistical Analysis on Landmark Data13:56

    Explore landmark data with regression, ANOVA, and discriminant and canonical analyses to test group differences, assess size and shape, and reveal clustering in 3D morphometric datasets.

Requirements

  • Introductory college-level science, engineering or social science
  • Basic knowledge of statistics
  • Knowledge of shape or image analysis will be an added advantage

Description

Morphometrics has experienced a major revolution through the invention of coordinate-based methods, the discovery of the statistical theory of shape, and the computational realization of deformation grids. The ubiquitous application of fast personal computers and modern analytical tools have ushered in a new era of data analysis, permitting the exploration and visualization of large high-dimensional data sets along with exact statistical tests based on resampling procedures. This new morphometric approach has been termed geometric morphometrics as it preserves the geometry of the landmark configurations throughout the analysis and thus permits to represent statistical results as actual shapes or forms. Therefore, these lectures aim at teaching practically, the concept of statistical shape analysis from 3D images. To encourage learning by exploration; images, annotations and data reports from the hand study are made available for download.

Who this course is for:

  • Scientists or Biologists dealing with statistical shape or image analysis
  • Researchers in the field of forensics studies of human facial morphology such as face recognition, age estimation, sex dimorphism, and facial expression recognition
  • Students and researchers in evolutionary anthropology and cognitive science
  • Students and researchers hypothesizing on quantitative genetics and general-purpose species identification (such as in plants, animals and human)
  • Experts conducting research on disease diagnosis or in genetic disorder prediction
  • Students and researchers in orthodontics, phylogenetics, biomechanics and bioinformatics, neuroimaging analysis and bone histomorphometry
  • Students and researchers in geomorphometrics or terrain analysis

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