Classification
Classification or cluster analysis groups similar objects in distinct clusters (in the EM Field usually called classes) while minimizing the variance (of a certain parameter) within one cluster/class. During image processing, classification is used to find similar 2D images within a dataset, but also similar 3D structures within a set of structures.
Usage
A prerequisite for a feasible classification is a complexity reduction of the given input dataset. This can be achieved by performing a Principal Component Analysis (PCA). The PCA can be carried out either internally by the classification-logic or beforehand by the respective PCA logic. Additionally, the user has to define the input set and the number of expected classes/clusters. Now, the logic will split the dataset according to the information provided by the PCA into as many classes/clusters as determined aiming for an optimum of i) minimal variance within each class/cluster and ii) a maximized signal-to-noise ratio.
Modes
PCA
This classification mode uses an internal/external PCA to reduce the dataset's complexity before splitting it into a defined number of classes.
Parameters | Description |
---|---|
Eigen images location | Define, whether the Eigen images used for complexity reduction are generated on-the-fly internally (intern) or provided by an input from en external source (extern) |
→ Number of eigen images | How many Eigen images (and therefore dimensions) should be used as components during linear combination |
Split up method | Determine, whether large classes should be split into smaller classes i) to obtain classes containing a similar number of images/volumes (Cluster size) or ii) to minimize internal variance within each class, as measured by the cross-correlation-coefficients (cccVariance) |
Number of classes | Number of resulting classes/clusters |
Remove duplicated images | Duplicate images identified by the classification are removed |
Input | Description |
---|---|
Input | Stack of input images |
Pre Eigen Images | (Only available, if Eigen images location = extern; i.e. Eigen images precomputed with Principal Component Analysis (PCA) logic) Stack of Eigen images with the sum of all images and a mask as the last two images |
Output | Description |
---|---|
Output | Stack of all images with added/altered classID header information |
Sums | Stack of one image per class/cluster, which represents the average of all images within that class/cluster |
Output | Description |
---|---|
ClassID | ID of the class/cluster, the image belongs to |
Concept
Once clusters/classes of images are found, they can be averaged (see SumByClassNumber) to improve the signal-to-noise-ratio substantially.1)