PCA

Unless your dataset is exceptionally large (>400) you will have more dimensions in you dataset than samples. If you need to check, the number of dimensions is twice the number of points in your template (i.e. x and y coordinates in the picture), strictly speaking you can subtract up to 6 as a result of the alignment process but that only works if both you and the stats package understand rank-deficient matrices. In order to reduce that number of dimensions, and reduce the chance of over-fitting we use PCA, fortunately shape PCA is built into Psychomorph in the form of an ASM (Active Shape Model).

PCA (somewhat) explained - A long winded incoherent rant on the topic.

The PCA Menu

Build Shape PCA (ASM) – Build an Active Shape Model(ASM) from a set of templates
Batch Analyse Shape – Analyse a set of templates using an ASM and produce a set of PCA parameters
Batch Shape Reconstruct – Reconstruct a set of templates from a set of PCA parameters using the ASM
---
Build Image PCI – Build a Principal Components Image (PCI) model from a set of images
Batch Analyse Images – Analyse a set of images uinsg a PCI model and produce a set of PCA parameters
Batch Image Reconstruct – Reconstruct a set images from a PCI model and a set of PCA parameters.
PCA Options
-Mask Input Images – Mask images prior to bulding PCI model or analysing image sets.
-Mask Output Images – Mask images reconstructed from PCI model.
-Normalise Images – Pre-normalise images before building or analysing PCI model.
-Save Residual Images. – Save residuals from image analysis.