A unified framework for joint registration and segmentation
K. Ens and J. von Berg and S. Kabus and C. Lorenz and B. Fischer
  6914, 691406    (2008)

Accurate image registration is a necessary prerequisite for many diagnostic and therapy planning procedures where complementary information from different images has to be combined. The design of robust and reliable non- parametric registration schemes is currently a very active research area. Modern approaches combine the pure registration scheme with other image processing routines such that both ingredients may benefit from each other. One of the new approaches is the combination of segmentation and registration (segistration). Here, the segmentation part guides the registration to its wanted configuration, whereas on the other hand the registration does lead to an automatic segmentation. By joining these image processing methods it is possible to overcome some of the pitfalls of the individual methods. Here, we focus on the benefits of the registration part. In1 we presented a framework for non-parametric image registration improved through manual segmentation (weighted image registration). We demonstrated that this technique considerably improves registration results. A next step is the investigation of automatic segmentation schemes. In the current work, we present a novel unified framework for non-parametric registration combined with segmentation through active contours. In the literature, one may find various ways to combine these image processing routines. Here, we present the most promising approaches within the general framework. It is based on a variational formulation of both the registration and the segmentation part. The performance tests are carried out for magnet resonance brain images, which demonstrate the potential of the proposed methods.