Using marker-based deformable models for deriving anatomical models from medical image databases

The outcome of the application of a deformable model is dependent on its initialization and parameter settings. This is an obstacle to robust automatic segmentation. An approach to reducing the dependences is proposed in this paper based on geodesic topographic distance transforms from two manually or automatically placed markers. This is a novel method that uses object and background differentiation in deformable models via watershed theories. The implementation is based on efficient numerical methods. Experiments have shown that it is a highly promising approach. This approach has been applied to the determination of tissue characteristics in early childhood using a CT database of neonates. We believe that by relaxing dependences on the initialization and parameters this approach will enable a degree of automation needed for segmentation-based construction of organ models from large image databases, particularly when the markers can be determined automatically on the basis of a priori knowledge