Disjunctive Normal Level Set: An Efficient Parametric Implicit Method
This is an incremental improvement for researchers and practitioners in image segmentation, offering a more efficient parametric level set method.
The paper tackles the problem of slow convergence and sensitivity to initialization in level set methods for image segmentation by proposing the Disjunctive Normal Level Set (DNLS), which achieves faster convergence, maintains regularity, and scales efficiently with the number of objects.
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.