Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacem...

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm...

W e present the concept of non-rigid matching based o n demons, by reference to Maxwell’s demons. W e contrast this concept with the more conventional viewpoint of attraction. W e show that demons and attractive points are clearly distinct f o r large deformations, but also that they become similar for small displacements, encompassing techniques close to optical flow. We describe a general ite...

Non-rigid registration becomes more and more important in biomedical imaging applications. A novel non-rigid registration method based on diffusion model with demons algorithm is proposed in this paper. The moving image is considered as a deformable grid, and it is diffusing through the contours of the objects in itself, by the action of effectors, called demons, situated in these interfaces. I...

The goal of this paper is to present a novel recipe for deformable image registration under varying illumination, as a natural extension of the demons algorithm. This generalization is derived directly from the optical-flow constraints in a variational formulation. Furthermore, our approach provides a new mathematical interpretation of the demons algorithm via fixed-point iterations in a consis...

The power and validity of voxel based and tensor based morphometry methods depend on the accuracy of the brain image registration algorithms they employ. We propose a mutual information based quantitative evaluation method to compare the performance of two publicly available deformable registration packages: HAMMER and algorithms in the ITK package (FEM-Demons) The advantage of our approach is ...