title: Multi-figure anatomical objects for shape statistics.
abstract: Multi-figure m-reps allow us to represent and analyze a complex anatomical object by its parts, by relations among its parts, and by the object itself as a whole entity. This representation also enables us to gather either global or hierarchical statistics from a population of such objects. We propose a framework to train the statistics of multi-figure anatomical objects from real patient data. This training requires fitting multi-figure m-reps to binary characteristic images of training objects. To evaluate the fitting approach, we propose a Monte Carlo method sampling the trained statistics. It shows that our methods generate geometrically proper models that are close to the set of Monte Carlo generated target models and thus can be expected to yield similar statistics to that used for the Monte Carlo generation.
authors.affiliation: Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC 27599, USA. han@cs.unc.edu
authors.forename: Qiong
authors.initials: Q
authors.lastname: Han
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Pizer
authors.forename: Derek
authors.initials: D
authors.lastname: Merck
authors.forename: Sarang
authors.initials: S
authors.lastname: Joshi
authors.forename: Ja-Yeon
authors.initials: JY
authors.lastname: Jeong
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; 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; D008953:Models, Anatomic; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique