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

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 distr...

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Bibliographic Details
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
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Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2003.815864