Transcript General morphometric protocol Four simple steps to morphometric success Four steps • Data acquisition – images and landmarks • Remove shape variation and generate shape variables.

General morphometric protocol
Four simple steps to morphometric
success
Four steps
• Data acquisition – images and landmarks
• Remove shape variation and generate shape
variables – superimposition and TPS
• Perform statistical analyses to test
biological hypotheses – standard
multivariate analysis and resampling
methods
• Produce graphical depiction of results –
deformation grids, statistical plots, etc.
Data acquisition - images
• Transferring 3D to 2D depiction
• Many ways to go wrong
• Three things that don’t matter
– Location in plane
– Scale
– Rotation
Problems to avoid
• Paralax – pitch and roll
• “bendiness” – look for straight lines and
include points on these lines
• Articulated structures – can incorporate in
analysis or remove as noise, but easiest to
avoid problem in beginning
Avoiding image problems
• Standardize image acquisition procedure
• Independent quality check
Digitizing landmarks
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Homology
Type 1, 2, and 3 - sliding semilandmarks
Order is critical
Checking for errors and outliers
Symmetrical structures
Step two – remove nonshape
variation and generate shape
variables
• 3 types of nonshape variation – relative
position, scale, rotation
• Remove by a process called
superimposition via generalized Procrustes
analysis or GPA
Variation in images
Translation
Rotation
Scaling
Only shape variation left
Generate shape variables
Thin plate spline
Generates non-affine and affine components
referred to as partial warps and uniform
components
Affine and non-affine shape change
Shape coordinates
• Partial warps come in X and Y pairs, (2p-4)
• Uniform components also a pair, X and Y
• Combined referred to as the W (weight)
matrix
• Scores are coordinates of a point along
partial warp axes
• Nonsingular data matrix for multivariate
analysis of shape
Relative warps
• Can use PCA on W matrix to generate
relative warp scores and use these as data
matrix
• Useful for visualization of major axis of
shape variation