Image registration and segmentation are two important tasks in medical image analysis. However, the validation
of algorithms for non–linear registration in particular often poses significant challenges:
Anatomical labelling based on scans for the validation of segmentation algorithms is often not available, and
is tedious to obtain. One possibility to obtain suitable ground truth is to use anatomically labelled atlas images.
Such atlas images are, however, generally limited to single subjects, and the displacement field of the registration
between the template and an arbitrary data set is unknown. Therefore, the precise registration error cannot be
determined, and approximations of a performance measure like the consistency error must be adapted. Thus,
validation requires that some form of ground truth is available.
In this work, an approach to generate a synthetic ground truth database for the validation of image registration
and segmentation is proposed. Its application is illustrated using the example of the validation of a registration
procedure, using 50 magnetic resonance images from different patients and two atlases. Three different non–linear
image registration methods were tested to obtain a synthetic validation database consisting of 50 anatomically
labelled brain scans.