title: Projected generalized procrustes alignment.
abstract: When observing the 3D world through a 2D projection, rigid 3D rotation will result in an apparent deformation not accounted for in traditional shape analysis methodologies, e.g. those based on Generalized Procrustes Alignment and Principal Component Analysis. We propose using a 3D statistical model to infer relative depth to a 2D shape and consequently model the apparent deformation in a Procrustes alignment framework. We test our approach on vertebra shapes and show that it leads to a more compact and generalizable shape model, as well as to improvement in vertebra fracture prediction.
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Konstantin
authors.initials: K
authors.lastname: Chernoff
authors.forename: Mads
authors.initials: M
authors.lastname: Nielsen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016103:Spinal Fractures / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*