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Statistical shape models are powerful tools for image interpretation and shape analysis. A simple, yet effective, way of building such models is to capture the statistics of sampled point coordinates over a training set of example shapes. However, a major drawback of this approach is the need to establish a correspondence across the training set. In 2-D, a correspondence is often defined using a set...
We compare two methods for assessing the performance of groupwise non-rigid registration algorithms. The first approach, which has been described previously, utilizes a measure of overlap between ground-truth anatomical labels. The second, which is new, exploits the fact that, given a set of nonrigidly registered images, a generative statistical model of appearance can be constructed. We observe that...
We have described previously a method of automatically constructing statistical models of shape. The method treats model-building as an optimisation problem by re-parameterising each shape so as to minimise the description length of the training set. The approach requires an explicit parameterisation of each shape, which is straightforward in 2D, but non-trivial in 3D. It is necessary to provide some...
Degeneration and loss of articular cartilage in osteoarthritis (OA) is difficult to measure, because changes are small and localised. We present a method that uses statistical shape models of the knee bones to define an anatomically consistent frame of reference across a population, providing sensitive measures of cartilage morphology in anatomically equivalent regions of interest. Bone and cartilage...
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