A probabilistic demons algorithm for texture-rich image registration

Demons algorithm has attracted considerable attention from the image processing community for registering (i.e., matching/aligning) deformable objects or images. It is observed that this algorithm is particularly successful when it applies to nonrigid object having homogenous region, but often fails when the object of interest is rich in texture. This is mainly because the Demons algorithm tends to overfit the many spurious edges inside the texture-rich region, consequently leading to erroneous thermodynamic 'forces'. In this paper, we describe a probabilistic Demons algorithm that overcomes this problem. Our key idea is to re-formulate the deformable registration problem in Bayesian statistics framework. The result is a new and more robust Demons algorithm able to capture the essence (e.g., the mass) of a deformable image/object even it is rich in texture. This will significantly expand the applicable scopes of the traditional Demons algorithm. We give encouraging experimental results on real test images