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 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.
Kaus
Michael R
MR
Philips Research Laboratories, Sector Technical Systems, Hamburg, Germany. michael.kaus@philips.com
Pekar
Vladimir
V
Lorenz
Christian
C
Truyen
Roel
R
Lobregt
Steven
S
Weese
Jürgen
J
D000465:Algorithms; D004839:Epiphyses, Slipped / Q000000981:diagnostic imaging*; D005269:Femur / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging*; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods