In this article a new segmentation approach is described that is based on case-based reasoning and a combination of various established image processing concepts described in the current literature. Previously segmented data sets are used as anatomical models that represent the cases, called reference models. They describe the expected surface shape and representation of the organ in the data material. The segmentation task is solved by finding a reference model that is similar to the current data set and then by adapting the reference segmentation to the current data set. Image segmentation can be divided into the steps "determination of the image context", "selection and adjustment of the reference model", and "application of the model-based snake". The necessary interaction time was reduced by more than 60%, including postprocessing to correct for possible segmentation errors.
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