Multi-agent stochastic level set method in image segmentation

• A variational framework based on level set theory is introduced to find the global solution for image segmentation problem.
• A single and a multi-agent stochastic structure are studied in the aforementioned framework.
• Proof of convergence for single-agent model (active contour with stochastic fronts) is also provided in this article.
• Comparing with Chan–Vese model, higher chance to find the global solution is achieved.

A stochastic structure for single and multi-agent level set method is investigated in this article in an attempt to overcome local optima problems in image segmentation. Like other global optimization methods that take advantage of random operators and multi-individual search algorithms, the best agent in this proposed algorithm plays the role of leader in order to enable the algorithm to find the global solution. To accomplish this, the procedure employs a set of stochastic partial differential equations (SPDE), each one of which evolves based on its own stochastic dynamics. The agents are then compelled to simultaneously converge to the best available topology. Moreover, the stochastic dynamics of each agent extends the stochastic level set approach by using a multi source structure. Each source is a delta function centered on a point of evolving front. Lastly, while the computational costs of these methods are higher than the region-based level set method, the probability of finding the global solution is significantly increased.