A Unified Framework for Atlas Matching Using Active Appearance Models

We propose to use statistical models of shape and texture as deformable anatomical atlases. By training on sets of labelled examples these can represent both the mean structure and appearance of anatomy in medical images, and the allowable modes of deformation. Given enough training examples such a model should be able synthesise any image of normal anatomy. By finding the parameters which minimise the difference between the synthesised model image and the target image we can locate all the modelled structure. This potentially time consuming step can be solved rapidly using the Active Appearance Model (AAM). In this paper we describe the models and the AAM algorithm and demonstrate the approach on structures in MR brain cross-sections.