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The task of image analysis, especially in the bio-medical domain, must take into consideration the variation in shape and appearance of objects. The invariant presumption is that corresponding objects in all images are of one particular class so we can typify the contents of the image by training an entity that captures inter-subject variation as well as atrophies.
Statistical analysis of shapes [3] which obtains a model of deformation goes back a decade ago. The principles were later extended to sample the variation in pixel intensities (also commonly referred to as textures) to create a model of full variation that is able to synthesise full appearances [4] and their successful application to medical data has been frequently demonstrated [5]. The correlations between shape and intensity are learned using Principal Component Analysis [6] where much of the power of these principles lies.