Common categories of registration optimizers, associated with different registration strategies :
* '''ITK iconic algorithm''' optimizes iteratively a transformation. The optimization process is guided by the superposition improvement measured using a global similarity measure between the two 3D images. Choosing this measure, we assume a relationship between intensities of reference and moving images, and that this relationship is valid and identical in any point of the image. That weak assumption can lead to robustness issues when dealing with multi-modal registration.
* '''The Block-Matching algorithm''' (reference) is a hybrid method, using a similarity measure to establish correspondences between images, then using these correspondences to compute a global transformation for the whole image. Assuming that the relationship between intensities of reference and moving
image can be valid in a local neighborhood, blockmatching uses it to compare subparts of the 3D images to identify correspondences. This algorithm is the default one in Fijiyama.
* Fijiyama does not have a "geometric" registration algorithm yet. Principle of such algorithms is to extract features from each images, and to establish correspondences between extracted features. Such approach can lead to faster but less robust algorithms as it is difficult to define a feature extraction method that is modality-independant, object-independant, and scale-independant. But in the case of monomodal registration, such algorithms can provide great success (reference : BigStitcher).