Registration and segmentation are two most important problems in the
field of medical image analysis. Traditionally, they were treated as
separate problems. In this paper, we introduce a unified variational framework for simultaneously carrying out image segmentation and registration. Segmentation information is integrated into the process of registration in leading to a more stable and noise-tolerant shape evolution, while a diffusion model is used to infer the volumetric deformation across the image. One of the major advantages of our model is its robustness against image noise. We present several 2D examples on synthetic and real data.