title: DISJUNCTIVE NORMAL LEVEL SET: AN EFFICIENT PARAMETRIC IMPLICIT METHOD.
abstract: Level set methods are widely used for image segmentation because of their capability to handle topological changes. In this paper, we propose a novel parametric level set method called Disjunctive Normal Level Set (DNLS), and apply it to both two phase (single object) and multiphase (multi-object) image segmentations. The DNLS is formed by union of polytopes which themselves are formed by intersections of half-spaces. The proposed level set framework has the following major advantages compared to other level set methods available in the literature. First, segmentation using DNLS converges much faster. Second, the DNLS level set function remains regular throughout its evolution. Third, the proposed multiphase version of the DNLS is less sensitive to initialization, and its computational cost and memory requirement remains almost constant as the number of objects to be simultaneously segmented grows. The experimental results show the potential of the proposed method.
authors.affiliation: Department of Electrical and Computer Eng., University of Utah, United States.
authors.forename: Fitsum
authors.initials: F
authors.lastname: Mesadi
authors.affiliation: Faculty of Eng. and Natural Sciences, Sabanci University, Turkey.
authors.forename: Mujdat
authors.initials: M
authors.lastname: Cetin
authors.affiliation: Department of Electrical and Computer Eng., University of Utah, United States.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen
keywords: Level set method; multiphase level set; parametric level set method; segmentation