  Designing a registration framework for images that do not share the same
probability distribution is a major challenge in modern image analytics yet
trivial task for the human visual system (HVS). Discrepancies in probability
distributions, also known as \emph{drifts}, can occur due to various reasons
including, but not limited to differences in sequences and modalities (e.g.,
MRI T1-T2 and MRI-CT registration), or acquisition settings (e.g., multisite,
inter-subject, or intra-subject registrations). The popular assumption about
the working of HVS is that it exploits a communal feature subspace exists
between the registering images or fields-of-view that encompasses key
drift-invariant features. Mimicking the approach that is potentially adopted by
the HVS, herein, we present a representation learning technique of this
invariant communal subspace that is shared by registering domains. The proposed
communal domain learning (CDL) framework uses a set of hierarchical nonlinear
transforms to learn the communal subspace that minimizes the probability
differences and maximizes the amount of shared information between the
registering domains. Similarity metric and parameter optimization calculations
for registration are subsequently performed in the drift-minimized learned
communal subspace. This generic registration framework is applied to register
multisequence (MR: T1, T2) and multimodal (MR, CT) images. Results demonstrated
generic applicability, consistent performance, and statistically significant
improvement for both multi-sequence and multi-modal data using the proposed
approach ($p$-value$<0.001$; Wilcoxon rank sum test) over baseline methods.
