Multilevel Thresholding with Membrane Computing Inspired TLBO

The selection of optimal thresholds is still a challenging task for researchers in case of multilevel thresholding. Many swarm and evolutionary computation techniques have been applied for obtaining optimal values of thresholds. The performance of all these computation techniques is highly dependent on proper selection of algorithm-specific parameters. In this work, a new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher-learner-based-optimization (TLBO) algorithm. To prove the efficacy of proposed algorithm, it is applied to solve multilevel thresholding problem in which the Kapur's entropy criterion is considered as figure-of-merit. In this experiment, four benchmark test images are considered for multilevel thresholding. The optimal values of thresholds are obtained using TLBO, MC and particle swarm optimization (PSO) in addition to proposed algorithm to accomplish the comparative study. To support the superiority of proposed algorithm over others, various quantitative and qualitative results are presented in addition to statistical analysis.