  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.
