Statistical deformable model-based segmentation of image motion

We present a statistical method for the motion-based segmentation of deformable structures undergoing nonrigid movements. The proposed approach relies on vivo models describing the shape of interest, its variability, and its movement. The first model corresponds to a statistical deformable template that constrains the shape and its deformations. The second model is introduced to represent the optical flow field inside the deformable template. These two models are combined within a single probability distribution, which enables to derive shape and motion estimates using a maximum likelihood approach. The method requires no manual initialization and is demonstrated on synthetic data and on a medical X-ray image sequence.