Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

In this article is introduced an innovative segmentation methodology that is based on a two-dimensional (2D) histogram that permits to increase the quality of the segmented images. The 2D histogram is constructed using the Lateral Inhibition (LI) that helps maintain and remark different image features. To segment the image in the proposed approach, the 2D Rényi entropy is used, which is a multi-level thresholding technique. Since the complexity of the 2D Rényi entropy increases with the number of thresholds, it is necessary to use an efficient search mechanism. To perform this task, it is also proposed an improved version of the Electromagnetic Field Optimization (EFO) algorithm that employs the High Disruptive Polynomial Mutation (HDPM) to exploit the search space intensively. The proposed metaheuristic is called MEFO. In combination with the 2D Rényi entropy creates a robust mechanism able to find the optimal configuration of thresholds that permits an accurate classification of the information contained in the 2D histogram generated using the (LI). The performance of the MEFO is tested over the Berkeley Segmentation Dataset (BSDS100) that contains 100 images with different complexities. The experiments include quantitative, qualitative, and statistical tests that permit the MEFO's efficiency in both senses for image segmentation and for solving multidimensional real optimization problems. Moreover, different comparisons validate the capabilities of the proposed algorithms to segment the images properly.