Practical Morphometrics Analysis (3D Model)

Name: Practical Morphometrics Analysis (3D Model)
Rating: 4.2 (23 reviews)

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
Introduces morphometrics as the measurement of form using three-dimensional images, landmarks, and outlines, and explains geometric coordinates, length, breadth, outline-based analyses, and practical applications and limitations.
Landmark and Acquisition in 3D14:01
Explore three-dimensional landmark acquisition in morphometrics, distinguishing biological, mathematical, and sliding landmarks, and learn how identical landmark counts and order enable reliable comparisons, while addressing 3D imaging and measurement error.
Landmark Visualization7:11
Explore landmark visualization and morphometric analysis, centering landmarks, and applying principal component analysis to reveal shape variation with scores, eigenvalues, and 3D loadings.
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.

3D image reduction and conversion10:53
Learn to reduce large 3d images by compressing files, lowering polygon counts and textures, and converting between formats, including binary and non-binary encodings, for faster workflows.
3D digitization with meshlab8:13
Explore 3d digitization using meshlab, explain landmark formats (standard and as), CSP export, and how to capture and export x y z points, while noting meshlab's limitations.
3D digitization with phylonimbus8:25
Learn to digitize 3d landmarks with phylonimbus, create projects, import images, select binary landmarks, and export coordinates for analysis.
3D digitization with geomorph13:46
Learn to set up and use geomorph for 3d digitization, including importing 3d images, selecting landmarks, and exporting ascii landmark data for morphometric analysis.
Sliding semilandmarks with Viewbox - Creating the template mesh29:28
Learn to slide semilandmarks along the surface with a template mesh in Viewbox, and create and apply a template to multiple specimens for morphometric analysis.
Sliding semilandmarks with Viewbox - Applying template to 3D mesh target7:37
Learn to apply a predefined template to 3D specimens using sliding semilandmarks in Viewbox, including aligning landmarks, adjusting displacement, and saving results.
Exporting landmarks data5:11
Master exporting landmarks data from a practical morphometrics workflow by selecting landmarks, decoding points, saving landmarks across multiple specimens, and organizing coordinates through copy and transpose operations.
Intro to error assessment4:41
Learn how to perform error assessment in morphometrics, using single- and multi-operator tests, repeated measurements, and representative specimens or proxies.

3D landmarks visualization - GPA and PCA15:59
Explore 3d landmarks visualization using generalized procrustes analysis and principal component analysis, addressing landmark data acquisition, error assessment, and visualization workflows for morphometric analysis.
Error assessment with Procrustes ANOVA17:05
Explore error assessment in morphometric analysis using Procrustes ANOVA, covering landmark data categorization, data labeling, and color coding to compare shapes accurately.
3D landmarks visualization in PAST8:13
Learn to visualize 3d landmarks in Past, import and clean data, create groups and categories, and extract principal components to interpret morphometric patterns.

ANOVA13:58
Master practical morphometrics analysis by preparing landmark data, selecting specimens, and running anova to test group differences and interpret results.
ANOSIM and PERMANOVA2:32
Apply anosim and permanova to detect group differences in 3d morphometrics data, and assess how sample size and data quality affect statistical significance.
MANOVA13:02
Perform a MANOVA to assess how central size and landmark-based size and shape influence morphological variation, interpret elementary effects and significance, and compare normalization approaches.
Regression Analysis4:05
Perform regression analysis in morphometrics, modeling central size against PC scores, identify independent and dependent variables, estimate regression coefficients, and interpret significance for morphology data.
Discriminant Analysis & Canonical Variate Analysis7:29
Learn how to perform discriminant analysis and canonical variate analysis on morphometric data, including decisions for single versus multiple groups and interpreting classification results and validation.
Clustering1:41
Explore clustering of 3D morphometric data, select methods like single linkage, compute distances in a matrix, and classify specimens into species based on variable measurements.
Euclidean Distance Matrix Analysis (EDMA)12:06
Explore euclidean distance matrix analysis (edma) for 3-dimensional morphometrics by computing landmark distances to analyze shapes and support classification of specimens.

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

Practical Morphometrics Analysis (3D Model)

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 43min

Landmark and Acquisition in 3D8 lectures • 1hr 28min

Visualizing 3D Landmarks3 lectures • 41min

Statistical Analysis on 3D Landmark Data7 lectures • 55min

Requirements

Description

Who this course is for: