The construction of such an appearance model from a set of images depends on the existence of a dense spatial correspondence across the set. In many manual or semiautomatic methods of model building, this dense correspondence is extrapolated and interpolated from the correspondence of some set of anatomically or userrelevant landmark points. In the automatic method that will be used here, the dense correspondence is given directly as the output of the NRR algorithm. Hence the relevant landmark positions in this case are in effect as dense as the pixels/voxels in the images registered.
13#13

In either case, the shape variation is represented in terms of the motions of these sets of landmark points. Using the notation of Cootes [#!Cootes_ECCV_1998!#], the shape (configuration of landmark points) of a single example can be represented as a vector 14#14 formed by concatenating the coordinates of the positions of all the landmark points for that example. The texture is represented by a vector 15#15, formed by concatenating the image values for the shapefree texture sampled from the image.
In the simplest case, we model the variation of shape and texture
in terms of multivariate gaussian distributions, using Principal
Component Analysis (PCA) [#!pca_joliffe!#]. We hence obtain
linear statistical models of the form:
16#16  17#17  18#18  
19#19  17#17  20#20  (2) 
In generative mode, the input shape ( 21#21) and ( 22#22) texture parameters can be varied continuously, allowing the generation of sets of images whose statistical distribution matches that of the model we have constructed.
In many cases, the variations of shape and texture are correlated.
If this correlation is taken into account, we then obtain a
combined statistical model of the more general form:
16#16  17#17  27#27  
19#19  17#17  28#28  (3) 
In many cases, we wish to distinguish between the meaningful shape variation of the objects under consideration, and that apparent variation in shape that is due to the positioning of the object within the image (the pose of the imaged object). In that case, the appearance model is generated from an (affinely) aligned set of images. Point positions 32#32 in the original image frame are then obtained by applying the relevant pose transformation 33#33:
34#34  (4) 
In an analogous manner, we can also normalise the image set with respect to the mean image intensities and image variance,
38#38  (5) 
For further details as regards the exact implementation of appearance models, see [#!Cootes_ECCV_1998!#,#!Edwards!#].
As noted above, a meaningful dense groupwise correspondence is required before an appearance model can be built. One way to obtain such a correspondence is by extrapolating from expert annotation. However, this annotation process is extremely timeconsuming and subjective, particularly for 3D data.
The output of groupwise NRR is such a correspondence, hence it was a natural next step to build automatic statistical models using the results of NRR algorithms [#!Frangi!#,#!Rueckert!#].
This link between registration and modelling is further exploited in the Minimum Description Length (MDL) [#!IPMI_2005_ISBE!#] algorithm for nonrigid registration, where modelling becomes an integral part of the registration process. This latter work will be one of the registration strategies used later in this paper.