  This paper presents a framework for automatically learning shape and
appearance models for medical (and certain other) images. It is based on the
idea that having a more accurate shape and appearance model leads to more
accurate image registration, which in turn leads to a more accurate shape and
appearance model. This leads naturally to an iterative scheme, which is based
on a probabilistic generative model that is fit using Gauss-Newton updates
within an EM-like framework. It was developed with the aim of enabling
distributed privacy-preserving analysis of brain image data, such that shared
information (shape and appearance basis functions) may be passed across sites,
whereas latent variables that encode individual images remain secure within
each site. These latent variables are proposed as features for
privacy-preserving data mining applications.
  The approach is demonstrated qualitatively on the KDEF dataset of 2D face
images, showing that it can align images that traditionally require shape and
appearance models trained using manually annotated data (manually defined
landmarks etc.). It is applied to MNIST dataset of handwritten digits to show
its potential for machine learning applications, particularly when training
data is limited. The model is able to handle ``missing data'', which allows it
to be cross-validated according to how well it can predict left-out voxels. The
suitability of the derived features for classifying individuals into patient
groups was assessed by applying it to a dataset of over 1,900 segmented
T1-weighted MR images, which included images from the COBRE and ABIDE datasets.
