Knowledge Driven Regularization of the Deformation Field for PDE Based Non-Rigid Registration Algorithms

We propose a novel method to model local regularization of medical image registration. The regularization model incorporates information from two different knowledge sources: 1. statistical aspect, considering regularization as a machine learning problem and 2. anatomical aspect, extracting predominant anatomical structures and modeling the ROI as composition of anatomical objects. Finally a link function is proposed to combine information from above stated knowledge sources. The method was trained and evaluated on a set of five CT lung scan pairs and on the EMPIRE10 dataset.