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.
Automated 3D PDM construction using deformable models
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 1 ; 566-572 vol.1
2001-01-01
835037 byte
Conference paper
Electronic Resource
English
Automated 3D PDM Construction Using Deformable Models
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