We describe a method for automatically building statistical shape models from a training set of exam- ple boundaries / surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of qandmarks manually on each training image, which is time-consuming and subjective in 2D, and almost impossible in 3D. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the best model. We define best as that which min- imizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of 2D boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking - the current gold standard. We also show that the method can be extended straightforwardly to 3D.

Polyethylene wear in the acetabular components of hip prostheses is implicated in loosening and failure. Radiographic measurement of wear is used to identify patients at risk and to assess prosthesis designs. This paper focuses on analysis of prostheses with cemented acetabular cups from anteroposterior radiographs. The articular surface of the femoral head and the acetabular rim marker are modelled as spherical and circular respectively, resulting in elliptical image projections. Methods for automatically localising these structures in radiographs are presented using robust ellipse fitting and various error functions. Special attention is paid to the acetabular marker since this often projects as a highly eccentric ellipse. Robust fitting enables successful localisation in the presence of clutter without the need for user interaction. Finally, the use of these ellipses as reference structures for wear estimation is investigated and the effect of eccentricity errors is highlighted. 1

Abstract not found