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Abstract:
We present a methodology for incorporating prior knowledge
on class probabilities into the registration process. By using knowledge
from the imaging modality, pre-segmentations, and/or probabilistic atlases,
we construct vectors of class probabilities for each image voxel. By
defining new image similarity measures for distribution-valued images,
we show how the class probability images can be nonrigidly registered in
a variational framework. An experiment on nonrigid registration of MR
and CT full-body scans illustrates that the proposed technique outperforms
standard mutual information (MI) and normalized mutual information
(NMI) based registration techniques when measured in terms of
target registration error (TRE) of manually labeled fiducials.
