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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
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? |