Automated 3-D PDM construction from segmented images using deformable models

• Published in: IEEE transactions on medical imaging 2003-08, Vol.22 (8), p.1005-1013
• Main Authors: Kaus, M.R., Pekar, V., Lorenz, C., Truyen, R., Lobregt, S., Weese, J.
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Computed tomography; Construction; Deformable models; Deformation; Epiphyses, Slipped - diagnostic imaging; Femur - diagnostic imaging; Formability; Humans; Image analysis; Image processing; Image recognition; Image segmentation; Imaging, Three-Dimensional - methods; Landmarks; Learning; Lumbar Vertebrae - diagnostic imaging; Mathematical models; Medical sciences; Models, Biological; Pattern Recognition, Automated; Principal component analysis; Product data management; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Robustness; Sensitivity and Specificity; Shape; Statistical analysis; Tomography, X-Ray Computed - methods
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.815864

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.