Automated 3-D PDM construction from segmented images using deformable models |
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Authors: | Kaus Michael R Pekar Vladimir Lorenz Christian Truyen Roel Lobregt Steven Weese Jürgen |
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Affiliation: | Philips Research Laboratories, Sector Technical Systems, Hamburg, Germany. michael.kaus@philips.com |
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Abstract: | In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes. |
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