Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy

Abstract

Multilevel thresholding (MLT) is one of the most widely used methods in image segmentation. However, the exhaustive search method is computationally time consuming for selecting the optimal thresholds. Consequently, heuristic algorithms are extensively used to reduce the complexity of the MLT problem. In this paper, an efficient Exchange Market Algorithm (EMA) is proposed to segment images using minimum cross entropy thresholding method. In the EMA, a market risk variable is used to balance the exploration and exploitation capabilities of the algorithm. Moreover, the local search capability is strengthened by the search and absorbent operators of EMA. Meanwhile, the most competent shareholders of EMA retain their best rank without undergoing any changes in their shares. These help in reducing the computational time. The proposed EMA based MLT is tested on benchmark and brain images with different threshold levels. Additionally, EMA approach is compared with other well-known algorithms such as, genetic algorithm, particle swarm optimization, bacterial foraging algorithm, firefly algorithm, honey bee mating optimization and teaching–learning based optimization. The experimental results show that the proposed EMA approach provides better outcomes than other algorithms.