title: DISJUNCTIVE NORMAL SHAPE MODELS.
abstract: A novel implicit parametric shape model is proposed for segmentation and analysis of medical images. Functions representing the shape of an object can be approximated as a union ofNpolytopes. Each polytope is obtained by the intersection ofMhalf-spaces. The shape function can be approximated as a disjunction of conjunctions, using the disjunctive normal form. The shape model is initialized using seed points defined by the user. We define a cost function based on the Chan-Vese energy functional. The model is differentiable, hence, gradient based optimization algorithms are used to find the model parameters.
authors.affiliation: Department of Electrical and Computer Engineering, University of Utah, United States ; Scientific Computing and Imaging Institute, University of Utah, United States.
authors.forename: Nisha
authors.initials: N
authors.lastname: Ramesh
authors.affiliation: Department of Electrical and Computer Engineering, University of Utah, United States ; Scientific Computing and Imaging Institute, University of Utah, United States.
authors.forename: Fitsum
authors.initials: F
authors.lastname: Mesadi
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Turkey.
authors.forename: Mujdat
authors.initials: M
authors.lastname: Cetin
authors.affiliation: Department of Electrical and Computer Engineering, University of Utah, United States ; Scientific Computing and Imaging Institute, University of Utah, United States ; Faculty of Engineering and Natural Sciences, Sabanci University, Turkey.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen
keywords: Chan-Vese; disjunctive normal form; implicit; parametric; shape model