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This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2D and 3D medical images. The models are derived from the statistics of sets of labelled images of examples of the objects. Each model consists of a flexible shape template, describing how important points of the objects can vary, and statistical models of the expected grey levels in regions around each model point. The shape models are parameterised in such a way as to allow only legal configurations. The models have proved useful in a wide variety of applications. We describe how they can be used in local image search and give examples of their application in medical image segmentation. We describe how 2D models can be used to segment 3D objects in volume images and to track structures in image sequences. We also describe how to generate full 3D models and illustrate their use to segment 3D Magnetic Resonance images of the brain.
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