Abstract: A computer-implemented method for generating anatomical data associated with a musculoskeletal joint, the method comprising: receiving as input variation data representing change with time of a musculoskeletal joint of interest caused by a condition; receiving as input patient data representing the musculoskeletal joint of interest of a patient at a current time; and processing the variation data and the patient data to generate said anatomical data, wherein said anatomical data comprises data indicating the musculoskeletal joint of interest of the patient at a predetermined time different to the current time.

Abstract: A computer-implemented method for identifying features in an image. The method comprises fitting a plurality of second models to the image, the plurality of second models together modelling a region of interest, wherein each part of the region of interest is modelled by at least two of the plurality of second models; and identifying the features in the image based upon the fit of the plurality of second models.

Abstract: A computer-implemented method of generating a model that models a class of objects. The method comprises, for each of a plurality of objects of the class of objects, receiving a first two-dimensional image of the object and first acquisition data, receiving a second two-dimensional image of the object and second acquisition data and receiving data indicating a plurality of points of interest within the object. The first and second two-dimensional images are combined based upon the first and second acquisition data to generate three-dimensional image data, the three-dimensional image data including data based upon the data indicating a plurality of points of interest and the generated three-dimensional image data for each of the objects of the class of objects is processed to generate the model.

Abstract: A statistical shape model is built by automatically establishing correspondence between a set of two dimensional shapes or three dimensional shapes. A parameterization of each shape is determined and, a statistical shape model is built using the parameterization. An objective function is used to provide an output which indicates the quality of the statistical shape model. By performing these steps repeatedly for different parameterizations and comparing the quality of the resulting statistical shape models using output of the objective function to determine which parameterization provides the statistical shape model having the best quality, the output of the objective function is a measure of the quantity of information required to code the set of shapes using the statistical shape model.