We present the Active Registration Model (ARM) that couples medical image registration with regularization using a statistical model of intensity. Inspired by Active Appearance Models (AAMs), the statistical model is embedded in the registration procedure as a regularization term that penalize differences between a target image and a synthesized model reconstruction of that image. We demonstrate that the method generalizes AAMs to 3D images, many different transformation models, and many different gradient descent optimization methods. The method is validated on magnetic resonance images of human brains.
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