Image registration is one of the most common research areas in medical image processing. It is required for
example for image fusion, motion estimation, patient positioning, or generation of medical atlases.
In most intensity-based registration approaches, parameters have to be determined, most commonly a parameter
indicating to which extend the transformation is required to be smooth. Its optimal value depends on multiple
factors like the application and the occurrence of noise in the images, and may therefore vary from case to
case. Moreover, multi-scale approaches are commonly applied on registration problems and demand for further
adjustment of the parameters.
In this paper, we present a landmark-based approach for automatic parameter optimization in non-linear
intensity-based image registration. In a first step, corresponding landmarks are automatically detected in the
images to match. The landmark-based target registration error (TRE), which is shown to be a valid metric for
quantifying registration accuracy, is then used to optimize the parameter choice during the registration process.
The approach is evaluated for the registration of lungs based on 22 thoracic 4D CT data sets. Experiments show
that the TRE can be reduced on average by 0.07 mm using automatic parameter optimization.