title: Comparing the similarity of statistical shape models using the Bhattacharya metric.
abstract: A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.
authors.affiliation: Division of Imaging Science and Biomedical Engineering, University of Manchester, M13 9PT, UK. kola.babalola@manchester.ac.uk
authors.forename: K O
authors.initials: KO
authors.lastname: Babalola
authors.forename: T F
authors.initials: TF
authors.lastname: Cootes
authors.forename: B
authors.initials: B
authors.lastname: Patenaude
authors.forename: A
authors.initials: A
authors.lastname: Rao
authors.forename: M
authors.initials: M
authors.lastname: Jenkinson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique