title: Geometrically proper models in statistical training.
abstract: In deformable model segmentation, the geometric training process plays a crucial role in providing shape statistical priors and appearance statistics that are used as likelihoods. Also, the geometric training process plays a crucial role in providing shape probability distributions in methods finding significant differences between classes. The quality of the training seriously affects the final results of segmentation or of significant difference finding between classes. However, the lack of shape priors in the training stage itself makes it difficult to enforce shape legality, i.e., making the model free of local self-intersection or creases. Shape legality not only yields proper shape statistics but also increases the consistency of parameterization of the object volume and thus proper appearance statistics. In this paper we propose a method incorporating explicit legality constraints in training process. The method is mathematically sound and has proved in practice to lead to shape probability distributions over only proper objects and most importantly to better segmentation results.
authors.affiliation: Medical Image Display & Analysis Group, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
authors.forename: Qiong
authors.initials: Q
authors.lastname: Han
authors.forename: Derek
authors.initials: D
authors.lastname: Merck
authors.forename: Josh
authors.initials: J
authors.lastname: Levy
authors.forename: Christina
authors.initials: C
authors.lastname: Villarruel
authors.forename: James N
authors.initials: JN
authors.lastname: Damon
authors.forename: Edward L
authors.initials: EL
authors.lastname: Chaney
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Pizer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000000981:diagnostic imaging*; 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; D001743:Urinary Bladder / Q000000981:diagnostic imaging*