This is an old revision of the document!
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.
Usage
The PCA is a prerequisite for data complexity reduction in order to perform a subsequent Classification. It
Example
Process
Parameters | Description |
---|---|
Dimension | Number of calculated dimensions (Eigen images) |
Eigenimages | Use external eigen images |
Using mask | Use an external mask |
Input | Description |
---|---|
Input | Stack of input images |
Eigen Images | External Eigen images |
Mask | External mask |
Output | Description |
---|---|
Eigenimages | Stack of generated Eigen images |
New/Changed Header Values | Description |
---|---|
headerValue1 | what does it say? how is it changed? |
headerValue2 | what does it say? how is it changed? |
headerValue3 | what does it say? how is it changed? |
headerValue4 | what does it say? how is it changed? |