======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 [[:eyes:logics: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.

|< 100% 30% >|
^ 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 |

|< 100% 30% >|
^ Input   ^ Description ^
| Input   | Stack of input images |
| //Pre Eigen Images// | (Only available, if //Eigen images location = extern; i.e. Eigen images precomputed with [[eyes:logics:PCA]] logic//) Stack of Eigen images with the sum of all images and a mask as the last two images |

|< 100% 30% >|
^ 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 |

|< 100% 30% >|
^ 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 [[eyes:logics:SumByClassNumber]]) to improve the signal-to-noise-ratio substantially.((van Heel, M. (1984). Multivariate statistical classification of noisy images (randomly oriented biological macromolecules). Ultramicroscopy, 13(1-2), 165–83. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/6382731))