Abstract
Even though the level set method is driving progress in image segmentation, its performance is still affected adversely in the presence of severe intensity inhomogeneity and multi-noise. Therefore, developing an accurate, robust, and fast technique to solve this becomes a pressing need. In this paper, we propose DSLSM, a novel unsupervised statistic level set model based on dual-kernel-induced local similarity measures, which not only can effectively handle severe intensity inhomogeneity and multi-noise, but also stabilizes and accelerates the evolution process. Specifically, we first define a novel dual-kernel-induced data term, which stacks multiple kernel functions and manipulates local similarity measurement via the Jensen–Shannon divergence, aiming at extracting more discriminative higher-dimension features and correlations between adjacent pixels. Then, a novel rectified morphological scheme is proposed to numerically implement our method, which not only precludes the issues of numerical instability and level set function degradation but significantly accelerates the contour evolution. In addition, an inverted pyramid-style scheduling strategy is proposed to explore the complementary utilization of local and non-local contextual features at different evolution phases to avoid tedious heuristics parameter tuning for the size of local regions. Extensive experiments on multiple challenging segmentation benchmarks demonstrate the effectiveness and superiority of our DSLSM over recent state-of-the-art methods.
•A novel dual-kernel-induced statistic level set model (DSLSM) is presented.•DKI energy is defined to combat severe intensity inhomogeneity and multi-noise.•A new rectified morphological scheme is proposed for robust and fast segmentation.•IPS strategy enhances the robustness of our DSLSM to different initializations.