Abstract
A framework for optimisation of specific criteria across the shape variability found in a population is proposed. The method is based on level set segmentation in the parametric space defined by Principal Component Analysis (PCA). The efficient narrow band evolution of the level set allows to search for the instances only in the neighborhood of the zero level set and not in the whole shape space. We are able to optimise any given criterion not to provide a single best fitting instance in the shape space, but rather to provide a group of instances that meet the criterion. This effectively defines a partition in the shape space, which can have any topology. The method works for data of any dimension, determined by the number of principal components retained. Results are shown on the application to shape analysis of human femora.KeywordsActive ContourShape SpaceActive Shape ModelMedical Image AnalysisStatistical Shape ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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