Statistical deformable model applied to anatomical landmark detection

We present a generic statistical deformable model for gray level medical images and propose to use it for template matching. Template matching methods usually rely on the arbitrary choice of a cost function and a template. Statistical models, on the other hand, allow us to derive optimal learning and matching algorithms from the modeling assumptions using likelihood maximization principles. We test the statistical deformable model on the automatic anatomical landmark detection in brain MRI, and compare its performance with the sum of squared differences (SSD), a reference cost-function for intensity-based template matching.