Disjunctive normal level set: an efficient parametric implicit method
Mesadi, Fitsum and Çetin, Müjdat and Taşdizen, Tolga (2016) Disjunctive normal level set: an efficient parametric implicit method. In: IEEE International Conference on Image Processing (ICIP 2016), Phoenix, AZ, USA
Official URL: http://dx.doi.org/10.1109/ICIP.2016.7533171
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.
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