Active Appearance Models (AAMs) provide a method of modelling the appearance of anatomical structures in medical images and locating them automatically. Although the AAM approach is computationally efficient, the models used to search unseen images for the structures of interest are large -typically the size of 100 images. This is perfectly practical for most 2-D images, but is currently impractical for 3-D images. We present a method for compressing the model information using a wavelet transform. The transform is applied to a set of training images in a shape-normalised frame, and coefficients of low variance across the training set are removed to reduce the information stored. An AAM is built from the training set using the wavelet coefficients rather than the raw intensities. We show that reliable image interpretation results can be obtained at a compression ratio of 20:1, which is sufficient to make 3-D AAMs a practical proposition.C. Taylor, A. Colchester (Eds.): MICCAI'99
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.