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 (PCA) of manually or semi-automatically placed corresponding landmarks on the learning shapes (point distribution models, PDM), 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 3D 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 CT data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.


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    Title :

    Automated 3D PDM construction using deformable models


    Contributors:
    Kaus, M.R. (author) / Pekar, V. (author) / Lorenz, C. (author) / Truyen, R. (author) / Lobregt, S. (author) / Richolt, J. (author) / Weese, J. (author)


    Publication date :

    2001-01-01


    Size :

    835037 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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