title: Learning active shape models for bifurcating contours.
abstract: Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. Automatic segmentation of tibial and femoral contours in knee X-ray images is investigated as a step towards reliable, quantitative radiographic analysis of osteoarthritis for diagnosis and assessment of progression. Results are presented using various features, the Mahalanobis distance, distance weighted K-nearest neighbours, and two relevance vector machine-based methods as quality of fit measure.
authors.affiliation: School of Applied Computing, University of Dundee, DD1 4HN Dundee, UK.
authors.forename: Matthias
authors.initials: M
authors.lastname: Seise
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: McKenna
authors.forename: Ian W
authors.initials: IW
authors.lastname: Ricketts
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Wigderowitz
mesh_terms: D000465:Algorithms; D001175:Arthrography / Q000379:methods; D001185:Artificial Intelligence; D006801:Humans; D007719:Knee Joint / Q000000981:diagnostic imaging*; D008046:Lip / Q000000981:diagnostic imaging*; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging*; 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