Principal Component Analysis (PCA)

This module yields so-called eigenimages from a stack of 2D images or 3D structures, thereby facilitating subsequent image classification to resolve heterogeneity.

The PCA is a prerequisite for data complexity reduction in order to perform a subsequent Classification; alternatively, it can also be directly performed within the classification logic. The PCA yields eigenimages (volumes in the 3D case) which represent the heterogeneity or covariance within the data. Pre-computing the PCA before classification allows to inspect the resulting eigenimages and to decide how many eigenimages should be used for image classification. In general, the first eigenimages will correspond to actual variations in structure and/or angular orientation, whereas later eigenimages will rather represent variations in the noise.